import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import librosa
import tensorflow as tf
import os
from tqdm import tqdm
import wave
import contextlib
import cv2
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras import datasets, layers, models
from sklearn import metrics
from sklearn.metrics import r2_score, confusion_matrix
import IPython.display as ipd
import random
2024-01-21 14:22:28.917033: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
click = 1
try:
X = np.load("X_t.npy")
Y = np.load("Y_t.npy")
except:
click = 0
os.listdir("Training_Files")
['.DS_Store', 'Beatles', 'Led Zeppelin', 'Nirvana', 'Pink Floyd', 'Elvis Presley', 'Beach Boys', 'Queen', 'ABBA', 'Bob Dylan']
def features_extractor(file_name):
audio, sample_rate = librosa.load(file_name)
mfccs_features = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=60)
mfccs_scaled_features = np.mean(mfccs_features.T,axis=0)
return mfccs_scaled_features
len(features_extractor("/Users/viralchitlangia/Documents/Songs_Audio_Trial/Training_Files/Beatles/ISawHerStandingThereRemastered2009.wav"))
60
y1, sr1 = librosa.load("/Users/viralchitlangia/Documents/Songs_Audio_Trial/Training_Files/Beatles/ISawHerStandingThereRemastered2009.wav")
librosa.feature.mfcc(y = y1, sr = sr1, n_mfcc = 40).shape
(40, 7493)
y2, sr2 = y2, sr2 = librosa.load("/Users/viralchitlangia/Documents/Songs_Audio_Trial/Training_Files/Beatles/WhileMyGuitarGentlyWeepsRemastered2009.wav")
librosa.feature.mfcc(y = y2, sr = sr2, n_mfcc = 40).shape
(40, 12275)
len(librosa.feature.mfcc(y = y2, sr = sr2, n_mfcc = 40)[0])
12275
fname = '/Users/viralchitlangia/Documents/Songs_Audio_Trial/Training_Files/Beatles/WhileMyGuitarGentlyWeepsRemastered2009.wav'
with contextlib.closing(wave.open(fname,'r')) as f:
frames = f.getnframes()
rate = f.getframerate()
duration = frames / rate
print((duration))
285.00172335600905
def duration(filename):
with contextlib.closing(wave.open(filename,'r')) as f:
frames = f.getnframes()
rate = f.getframerate()
duration = frames / rate
return int(duration)
def near_empty_array(x, n):
y = [x]
for i in range(0, n - 1):
y.append(0)
return y
near_empty_array(5, 4)
[5, 0, 0, 0]
np.append(features_extractor(fname), 20).tolist()
[-90.9020767211914, 90.09610748291016, -22.818819046020508, 40.834877014160156, 3.6339235305786133, 10.50902271270752, -5.8387346267700195, 8.07880687713623, 0.3058115541934967, -0.19161468744277954, -4.686412811279297, 3.4597251415252686, -2.014287233352661, 1.6238023042678833, -2.8573644161224365, -1.4839766025543213, -8.509678840637207, 1.2135590314865112, -9.312557220458984, -6.9956865310668945, -4.266140937805176, -1.599496603012085, -6.903672218322754, -3.521676778793335, -8.436915397644043, -0.20250433683395386, -6.002135753631592, 0.37704238295555115, -5.920943737030029, -5.659139633178711, -1.898746132850647, 2.9211552143096924, -3.8918182849884033, -3.6990182399749756, -5.4410271644592285, -1.9968183040618896, -6.6689677238464355, -0.46719056367874146, -2.307814836502075, -1.504570722579956, -3.611607789993286, 1.1021623611450195, -2.398678779602051, -0.9391394257545471, -2.6728131771087646, -1.6004353761672974, -5.713006019592285, 2.2308390140533447, -0.4296121895313263, -4.351404666900635, -3.5961544513702393, 3.0389599800109863, 1.7495242357254028, -0.9395294189453125, -3.709394693374634, -1.320820689201355, -5.4441094398498535, -4.134355068206787, -3.3126020431518555, -0.559026837348938, 20.0]
if click == 0:
X = []
Y = []
if click == 0:
for x in os.listdir("Training_Files"):
if x != ".DS_Store":
k = os.listdir("Training_Files/" + x)
for j in tqdm(range(0, len(k))):
if k[j] != ".DS_Store":
mfcc = features_extractor("Training_Files/" + x + "/" + k[j])
time = duration("Training_Files/" + x + "/" + k[j])
mfcc = np.append(mfcc, time)
X.append(mfcc.tolist())
Y.append(x)
if click == 0:
X = np.array(X)
Y = np.array(Y)
np.save("X_t.npy", X)
np.save("Y_t.npy", Y)
np.unique(Y)
array(['ABBA', 'Beach Boys', 'Beatles', 'Bob Dylan', 'Elvis Presley',
'Led Zeppelin', 'Nirvana', 'Pink Floyd', 'Queen'], dtype='<U13')
l = LabelEncoder()
l.fit(Y)
Y_t = l.transform(Y)
Y_t
array([2, 2, 2, ..., 3, 3, 3])
X_train, X_test, Y_train, Y_test = train_test_split(X, Y_t, test_size=0.40)
X_train.shape
(843, 61)
# model initialization
model = tf.keras.Sequential()
# adding the 1st and 2nd layer layer
model.add(tf.keras.layers.Flatten(input_shape=(61,)))
model.add(tf.keras.layers.Dense(256, activation = 'relu'))
model.add(tf.keras.layers.Dense(128, activation = 'relu'))
#__add__ additional Intermediate Dense layers here to create the output
model.add(tf.keras.layers.Dense(84, activation = 'leaky_relu'))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(56, activation = 'relu'))
model.add(tf.keras.layers.Dense(42, activation = 'relu'))
#model.add(tf.keras.layers.Dropout(0.3))
model.add(tf.keras.layers.Dense(36, activation = 'relu'))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(24, activation = 'relu'))
model.add(tf.keras.layers.Dense(18, activation = 'relu'))
#model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(15, activation = 'relu'))
#model.add(tf.keras.layers.Dropout(0.2))
#__output__layer with correct output shape and activation function[google if finding this difficult to get]
model.add(tf.keras.layers.Dense(9, activation = 'softmax'))
#model.summary()
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten (Flatten) (None, 61) 0
dense (Dense) (None, 256) 15872
dense_1 (Dense) (None, 128) 32896
dense_2 (Dense) (None, 84) 10836
dropout (Dropout) (None, 84) 0
dense_3 (Dense) (None, 56) 4760
dense_4 (Dense) (None, 42) 2394
dense_5 (Dense) (None, 36) 1548
dropout_1 (Dropout) (None, 36) 0
dense_6 (Dense) (None, 24) 888
dense_7 (Dense) (None, 18) 450
dense_8 (Dense) (None, 15) 285
dense_9 (Dense) (None, 9) 144
=================================================================
Total params: 70,073
Trainable params: 70,073
Non-trainable params: 0
_________________________________________________________________
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate = 1e-3),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
history = model.fit(X_train, Y_train, epochs=750,
validation_data=(X_test, Y_test))
Epoch 1/750
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/keras/backend.py:5612: UserWarning: "`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Softmax activation and thus does not represent logits. Was this intended? output, from_logits = _get_logits(
27/27 [==============================] - 2s 9ms/step - loss: 2.2309 - accuracy: 0.1684 - val_loss: 2.0783 - val_accuracy: 0.2349 Epoch 2/750 27/27 [==============================] - 0s 2ms/step - loss: 2.0192 - accuracy: 0.2467 - val_loss: 1.9579 - val_accuracy: 0.2278 Epoch 3/750 27/27 [==============================] - 0s 3ms/step - loss: 1.9000 - accuracy: 0.2835 - val_loss: 1.8655 - val_accuracy: 0.3363 Epoch 4/750 27/27 [==============================] - 0s 2ms/step - loss: 1.8117 - accuracy: 0.3310 - val_loss: 1.8435 - val_accuracy: 0.3363 Epoch 5/750 27/27 [==============================] - 0s 2ms/step - loss: 1.7488 - accuracy: 0.3796 - val_loss: 1.7757 - val_accuracy: 0.3452 Epoch 6/750 27/27 [==============================] - 0s 3ms/step - loss: 1.7170 - accuracy: 0.3938 - val_loss: 1.7661 - val_accuracy: 0.3559 Epoch 7/750 27/27 [==============================] - 0s 2ms/step - loss: 1.6410 - accuracy: 0.4164 - val_loss: 1.6688 - val_accuracy: 0.3968 Epoch 8/750 27/27 [==============================] - 0s 2ms/step - loss: 1.5512 - accuracy: 0.4579 - val_loss: 1.6331 - val_accuracy: 0.4039 Epoch 9/750 27/27 [==============================] - 0s 3ms/step - loss: 1.5217 - accuracy: 0.4520 - val_loss: 1.6295 - val_accuracy: 0.3932 Epoch 10/750 27/27 [==============================] - 0s 2ms/step - loss: 1.4840 - accuracy: 0.4662 - val_loss: 1.6166 - val_accuracy: 0.4021 Epoch 11/750 27/27 [==============================] - 0s 3ms/step - loss: 1.4681 - accuracy: 0.4603 - val_loss: 1.7658 - val_accuracy: 0.3665 Epoch 12/750 27/27 [==============================] - 0s 2ms/step - loss: 1.5047 - accuracy: 0.4591 - val_loss: 1.4927 - val_accuracy: 0.4395 Epoch 13/750 27/27 [==============================] - 0s 2ms/step - loss: 1.3604 - accuracy: 0.5208 - val_loss: 1.5547 - val_accuracy: 0.4342 Epoch 14/750 27/27 [==============================] - 0s 2ms/step - loss: 1.3485 - accuracy: 0.5089 - val_loss: 1.5514 - val_accuracy: 0.4520 Epoch 15/750 27/27 [==============================] - 0s 2ms/step - loss: 1.2896 - accuracy: 0.5255 - val_loss: 1.4593 - val_accuracy: 0.4609 Epoch 16/750 27/27 [==============================] - 0s 2ms/step - loss: 1.3032 - accuracy: 0.5196 - val_loss: 1.4924 - val_accuracy: 0.4733 Epoch 17/750 27/27 [==============================] - 0s 2ms/step - loss: 1.2702 - accuracy: 0.5350 - val_loss: 1.6040 - val_accuracy: 0.4431 Epoch 18/750 27/27 [==============================] - 0s 2ms/step - loss: 1.2555 - accuracy: 0.5326 - val_loss: 1.4062 - val_accuracy: 0.4786 Epoch 19/750 27/27 [==============================] - 0s 2ms/step - loss: 1.2342 - accuracy: 0.5421 - val_loss: 1.4464 - val_accuracy: 0.4804 Epoch 20/750 27/27 [==============================] - 0s 2ms/step - loss: 1.1799 - accuracy: 0.5635 - val_loss: 1.4213 - val_accuracy: 0.4840 Epoch 21/750 27/27 [==============================] - 0s 2ms/step - loss: 1.1299 - accuracy: 0.5741 - val_loss: 1.3660 - val_accuracy: 0.4964 Epoch 22/750 27/27 [==============================] - 0s 2ms/step - loss: 1.1253 - accuracy: 0.5813 - val_loss: 1.4517 - val_accuracy: 0.5107 Epoch 23/750 27/27 [==============================] - 0s 2ms/step - loss: 1.1352 - accuracy: 0.5801 - val_loss: 1.4085 - val_accuracy: 0.5089 Epoch 24/750 27/27 [==============================] - 0s 2ms/step - loss: 1.1189 - accuracy: 0.5848 - val_loss: 1.4072 - val_accuracy: 0.5089 Epoch 25/750 27/27 [==============================] - 0s 2ms/step - loss: 1.0908 - accuracy: 0.5907 - val_loss: 1.4463 - val_accuracy: 0.4769 Epoch 26/750 27/27 [==============================] - 0s 2ms/step - loss: 1.0607 - accuracy: 0.6002 - val_loss: 1.2595 - val_accuracy: 0.5730 Epoch 27/750 27/27 [==============================] - 0s 2ms/step - loss: 1.0316 - accuracy: 0.6311 - val_loss: 1.3228 - val_accuracy: 0.5498 Epoch 28/750 27/27 [==============================] - 0s 2ms/step - loss: 1.0194 - accuracy: 0.6192 - val_loss: 1.2565 - val_accuracy: 0.5694 Epoch 29/750 27/27 [==============================] - 0s 3ms/step - loss: 0.9904 - accuracy: 0.6168 - val_loss: 1.2872 - val_accuracy: 0.5836 Epoch 30/750 27/27 [==============================] - 0s 3ms/step - loss: 0.9879 - accuracy: 0.6287 - val_loss: 1.2841 - val_accuracy: 0.5979 Epoch 31/750 27/27 [==============================] - 0s 2ms/step - loss: 0.9417 - accuracy: 0.6655 - val_loss: 1.2634 - val_accuracy: 0.6210 Epoch 32/750 27/27 [==============================] - 0s 2ms/step - loss: 0.8820 - accuracy: 0.6773 - val_loss: 1.4339 - val_accuracy: 0.5623 Epoch 33/750 27/27 [==============================] - 0s 2ms/step - loss: 0.8699 - accuracy: 0.6916 - val_loss: 1.2147 - val_accuracy: 0.6299 Epoch 34/750 27/27 [==============================] - 0s 2ms/step - loss: 0.8377 - accuracy: 0.6987 - val_loss: 1.3832 - val_accuracy: 0.6032 Epoch 35/750 27/27 [==============================] - 0s 2ms/step - loss: 0.8631 - accuracy: 0.7023 - val_loss: 1.2369 - val_accuracy: 0.6121 Epoch 36/750 27/27 [==============================] - 0s 2ms/step - loss: 0.7568 - accuracy: 0.7343 - val_loss: 1.1901 - val_accuracy: 0.6566 Epoch 37/750 27/27 [==============================] - 0s 2ms/step - loss: 0.7107 - accuracy: 0.7426 - val_loss: 1.3033 - val_accuracy: 0.6192 Epoch 38/750 27/27 [==============================] - 0s 2ms/step - loss: 0.6933 - accuracy: 0.7367 - val_loss: 1.3322 - val_accuracy: 0.6566 Epoch 39/750 27/27 [==============================] - 0s 3ms/step - loss: 0.6862 - accuracy: 0.7521 - val_loss: 1.3402 - val_accuracy: 0.6139 Epoch 40/750 27/27 [==============================] - 0s 2ms/step - loss: 0.7298 - accuracy: 0.7378 - val_loss: 1.3216 - val_accuracy: 0.6548 Epoch 41/750 27/27 [==============================] - 0s 2ms/step - loss: 0.6978 - accuracy: 0.7568 - val_loss: 1.2447 - val_accuracy: 0.6833 Epoch 42/750 27/27 [==============================] - 0s 2ms/step - loss: 0.6072 - accuracy: 0.7675 - val_loss: 1.3619 - val_accuracy: 0.6726 Epoch 43/750 27/27 [==============================] - 0s 2ms/step - loss: 0.6573 - accuracy: 0.7782 - val_loss: 1.3163 - val_accuracy: 0.6423 Epoch 44/750 27/27 [==============================] - 0s 3ms/step - loss: 0.5581 - accuracy: 0.8114 - val_loss: 1.6847 - val_accuracy: 0.6335 Epoch 45/750 27/27 [==============================] - 0s 2ms/step - loss: 0.6017 - accuracy: 0.7865 - val_loss: 1.3643 - val_accuracy: 0.6406 Epoch 46/750 27/27 [==============================] - 0s 2ms/step - loss: 0.5663 - accuracy: 0.7983 - val_loss: 1.5263 - val_accuracy: 0.6530 Epoch 47/750 27/27 [==============================] - 0s 2ms/step - loss: 0.6076 - accuracy: 0.7794 - val_loss: 1.3316 - val_accuracy: 0.6815 Epoch 48/750 27/27 [==============================] - 0s 2ms/step - loss: 0.5112 - accuracy: 0.8102 - val_loss: 1.2632 - val_accuracy: 0.6975 Epoch 49/750 27/27 [==============================] - 0s 2ms/step - loss: 0.5024 - accuracy: 0.8185 - val_loss: 1.4600 - val_accuracy: 0.6690 Epoch 50/750 27/27 [==============================] - 0s 2ms/step - loss: 0.4467 - accuracy: 0.8493 - val_loss: 1.4405 - val_accuracy: 0.6940 Epoch 51/750 27/27 [==============================] - 0s 2ms/step - loss: 0.4166 - accuracy: 0.8529 - val_loss: 1.7584 - val_accuracy: 0.6815 Epoch 52/750 27/27 [==============================] - 0s 2ms/step - loss: 0.4906 - accuracy: 0.8209 - val_loss: 1.3596 - val_accuracy: 0.6762 Epoch 53/750 27/27 [==============================] - 0s 2ms/step - loss: 0.5086 - accuracy: 0.8090 - val_loss: 1.5776 - val_accuracy: 0.6655 Epoch 54/750 27/27 [==============================] - 0s 2ms/step - loss: 0.4682 - accuracy: 0.8458 - val_loss: 1.2807 - val_accuracy: 0.7260 Epoch 55/750 27/27 [==============================] - 0s 2ms/step - loss: 0.4103 - accuracy: 0.8493 - val_loss: 1.3087 - val_accuracy: 0.7171 Epoch 56/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3506 - accuracy: 0.8719 - val_loss: 1.4368 - val_accuracy: 0.7082 Epoch 57/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3682 - accuracy: 0.8648 - val_loss: 1.5397 - val_accuracy: 0.7100 Epoch 58/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3566 - accuracy: 0.8778 - val_loss: 1.4721 - val_accuracy: 0.7367 Epoch 59/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3376 - accuracy: 0.8731 - val_loss: 1.5414 - val_accuracy: 0.6886 Epoch 60/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3127 - accuracy: 0.8921 - val_loss: 1.5571 - val_accuracy: 0.7224 Epoch 61/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3434 - accuracy: 0.8719 - val_loss: 1.6903 - val_accuracy: 0.7242 Epoch 62/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3702 - accuracy: 0.8743 - val_loss: 1.4159 - val_accuracy: 0.6904 Epoch 63/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2903 - accuracy: 0.8909 - val_loss: 1.7148 - val_accuracy: 0.7046 Epoch 64/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2770 - accuracy: 0.9134 - val_loss: 1.8661 - val_accuracy: 0.6922 Epoch 65/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3163 - accuracy: 0.8814 - val_loss: 1.7670 - val_accuracy: 0.6922 Epoch 66/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3042 - accuracy: 0.8849 - val_loss: 1.5981 - val_accuracy: 0.7153 Epoch 67/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2386 - accuracy: 0.9170 - val_loss: 1.5692 - val_accuracy: 0.7331 Epoch 68/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2252 - accuracy: 0.9300 - val_loss: 1.7595 - val_accuracy: 0.6762 Epoch 69/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3219 - accuracy: 0.8992 - val_loss: 1.8673 - val_accuracy: 0.6904 Epoch 70/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3510 - accuracy: 0.8802 - val_loss: 1.6013 - val_accuracy: 0.7171 Epoch 71/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2348 - accuracy: 0.9193 - val_loss: 1.7731 - val_accuracy: 0.6975 Epoch 72/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2129 - accuracy: 0.9253 - val_loss: 1.8862 - val_accuracy: 0.6975 Epoch 73/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2830 - accuracy: 0.9051 - val_loss: 1.7763 - val_accuracy: 0.7064 Epoch 74/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2817 - accuracy: 0.9098 - val_loss: 1.7395 - val_accuracy: 0.7295 Epoch 75/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1856 - accuracy: 0.9348 - val_loss: 1.9753 - val_accuracy: 0.7171 Epoch 76/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2136 - accuracy: 0.9265 - val_loss: 1.6833 - val_accuracy: 0.7313 Epoch 77/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1446 - accuracy: 0.9537 - val_loss: 2.1296 - val_accuracy: 0.7028 Epoch 78/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1937 - accuracy: 0.9265 - val_loss: 1.8009 - val_accuracy: 0.7349 Epoch 79/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1849 - accuracy: 0.9324 - val_loss: 2.1563 - val_accuracy: 0.7349 Epoch 80/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1471 - accuracy: 0.9585 - val_loss: 2.1106 - val_accuracy: 0.6975 Epoch 81/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1348 - accuracy: 0.9597 - val_loss: 2.5593 - val_accuracy: 0.6797 Epoch 82/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1469 - accuracy: 0.9490 - val_loss: 2.1660 - val_accuracy: 0.7153 Epoch 83/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1711 - accuracy: 0.9478 - val_loss: 2.4314 - val_accuracy: 0.6833 Epoch 84/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1403 - accuracy: 0.9490 - val_loss: 2.3948 - val_accuracy: 0.7224 Epoch 85/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1412 - accuracy: 0.9490 - val_loss: 2.6475 - val_accuracy: 0.6868 Epoch 86/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1759 - accuracy: 0.9407 - val_loss: 2.2151 - val_accuracy: 0.6904 Epoch 87/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1400 - accuracy: 0.9514 - val_loss: 2.4839 - val_accuracy: 0.6940 Epoch 88/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1690 - accuracy: 0.9490 - val_loss: 1.9097 - val_accuracy: 0.7367 Epoch 89/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2458 - accuracy: 0.9336 - val_loss: 1.5784 - val_accuracy: 0.7473 Epoch 90/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1467 - accuracy: 0.9573 - val_loss: 1.9773 - val_accuracy: 0.7189 Epoch 91/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1207 - accuracy: 0.9680 - val_loss: 2.2338 - val_accuracy: 0.7100 Epoch 92/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1025 - accuracy: 0.9703 - val_loss: 2.3225 - val_accuracy: 0.6922 Epoch 93/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1358 - accuracy: 0.9573 - val_loss: 2.3724 - val_accuracy: 0.7064 Epoch 94/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1554 - accuracy: 0.9549 - val_loss: 2.4760 - val_accuracy: 0.6744 Epoch 95/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2219 - accuracy: 0.9288 - val_loss: 2.0524 - val_accuracy: 0.7189 Epoch 96/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1302 - accuracy: 0.9573 - val_loss: 2.3122 - val_accuracy: 0.7153 Epoch 97/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0803 - accuracy: 0.9786 - val_loss: 2.5526 - val_accuracy: 0.7117 Epoch 98/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0751 - accuracy: 0.9751 - val_loss: 2.6208 - val_accuracy: 0.7278 Epoch 99/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0498 - accuracy: 0.9893 - val_loss: 2.5974 - val_accuracy: 0.7295 Epoch 100/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0476 - accuracy: 0.9870 - val_loss: 2.5990 - val_accuracy: 0.7260 Epoch 101/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0552 - accuracy: 0.9834 - val_loss: 2.6552 - val_accuracy: 0.7295 Epoch 102/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1570 - accuracy: 0.9668 - val_loss: 2.6024 - val_accuracy: 0.6690 Epoch 103/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1271 - accuracy: 0.9620 - val_loss: 2.2812 - val_accuracy: 0.7206 Epoch 104/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0750 - accuracy: 0.9751 - val_loss: 2.5919 - val_accuracy: 0.6975 Epoch 105/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0912 - accuracy: 0.9751 - val_loss: 2.8756 - val_accuracy: 0.6815 Epoch 106/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3275 - accuracy: 0.9300 - val_loss: 2.3939 - val_accuracy: 0.6690 Epoch 107/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3066 - accuracy: 0.9063 - val_loss: 1.9142 - val_accuracy: 0.6922 Epoch 108/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1935 - accuracy: 0.9537 - val_loss: 2.1139 - val_accuracy: 0.7011 Epoch 109/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1110 - accuracy: 0.9715 - val_loss: 2.0702 - val_accuracy: 0.7189 Epoch 110/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0687 - accuracy: 0.9798 - val_loss: 2.3422 - val_accuracy: 0.7171 Epoch 111/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0417 - accuracy: 0.9881 - val_loss: 2.4723 - val_accuracy: 0.7367 Epoch 112/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0487 - accuracy: 0.9881 - val_loss: 2.3195 - val_accuracy: 0.7260 Epoch 113/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0442 - accuracy: 0.9905 - val_loss: 2.7354 - val_accuracy: 0.7064 Epoch 114/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0760 - accuracy: 0.9810 - val_loss: 3.0673 - val_accuracy: 0.7064 Epoch 115/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0597 - accuracy: 0.9870 - val_loss: 2.7083 - val_accuracy: 0.7046 Epoch 116/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0492 - accuracy: 0.9870 - val_loss: 2.6727 - val_accuracy: 0.7260 Epoch 117/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0749 - accuracy: 0.9798 - val_loss: 3.1423 - val_accuracy: 0.6957 Epoch 118/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0751 - accuracy: 0.9798 - val_loss: 3.0833 - val_accuracy: 0.6815 Epoch 119/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0483 - accuracy: 0.9858 - val_loss: 2.7395 - val_accuracy: 0.7384 Epoch 120/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1059 - accuracy: 0.9715 - val_loss: 2.5292 - val_accuracy: 0.7224 Epoch 121/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0813 - accuracy: 0.9775 - val_loss: 2.8451 - val_accuracy: 0.7028 Epoch 122/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1350 - accuracy: 0.9715 - val_loss: 2.3971 - val_accuracy: 0.7028 Epoch 123/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0775 - accuracy: 0.9751 - val_loss: 2.7584 - val_accuracy: 0.6815 Epoch 124/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1123 - accuracy: 0.9703 - val_loss: 2.4340 - val_accuracy: 0.7242 Epoch 125/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1581 - accuracy: 0.9597 - val_loss: 2.5691 - val_accuracy: 0.7011 Epoch 126/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1133 - accuracy: 0.9668 - val_loss: 2.7336 - val_accuracy: 0.7100 Epoch 127/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0739 - accuracy: 0.9775 - val_loss: 2.4535 - val_accuracy: 0.7082 Epoch 128/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0466 - accuracy: 0.9881 - val_loss: 2.8451 - val_accuracy: 0.7171 Epoch 129/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0487 - accuracy: 0.9858 - val_loss: 2.3166 - val_accuracy: 0.7206 Epoch 130/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0975 - accuracy: 0.9751 - val_loss: 2.8314 - val_accuracy: 0.7028 Epoch 131/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0850 - accuracy: 0.9786 - val_loss: 2.6808 - val_accuracy: 0.7153 Epoch 132/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1320 - accuracy: 0.9692 - val_loss: 2.2271 - val_accuracy: 0.7189 Epoch 133/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0586 - accuracy: 0.9786 - val_loss: 2.0880 - val_accuracy: 0.7189 Epoch 134/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1306 - accuracy: 0.9739 - val_loss: 2.1496 - val_accuracy: 0.7171 Epoch 135/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1912 - accuracy: 0.9597 - val_loss: 2.0640 - val_accuracy: 0.7278 Epoch 136/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0789 - accuracy: 0.9751 - val_loss: 1.8647 - val_accuracy: 0.7384 Epoch 137/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0437 - accuracy: 0.9858 - val_loss: 2.1268 - val_accuracy: 0.7242 Epoch 138/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0680 - accuracy: 0.9822 - val_loss: 2.5175 - val_accuracy: 0.7135 Epoch 139/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0564 - accuracy: 0.9858 - val_loss: 2.1744 - val_accuracy: 0.7420 Epoch 140/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0759 - accuracy: 0.9846 - val_loss: 2.2400 - val_accuracy: 0.7189 Epoch 141/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0349 - accuracy: 0.9905 - val_loss: 2.8518 - val_accuracy: 0.7064 Epoch 142/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0437 - accuracy: 0.9870 - val_loss: 3.0157 - val_accuracy: 0.7064 Epoch 143/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0607 - accuracy: 0.9858 - val_loss: 2.7773 - val_accuracy: 0.7224 Epoch 144/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0401 - accuracy: 0.9905 - val_loss: 3.1259 - val_accuracy: 0.7011 Epoch 145/750 27/27 [==============================] - 0s 3ms/step - loss: 0.1470 - accuracy: 0.9609 - val_loss: 2.3876 - val_accuracy: 0.7082 Epoch 146/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0803 - accuracy: 0.9763 - val_loss: 2.7426 - val_accuracy: 0.6833 Epoch 147/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0358 - accuracy: 0.9929 - val_loss: 2.4578 - val_accuracy: 0.7331 Epoch 148/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0335 - accuracy: 0.9905 - val_loss: 2.7366 - val_accuracy: 0.6993 Epoch 149/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0630 - accuracy: 0.9822 - val_loss: 2.6638 - val_accuracy: 0.7135 Epoch 150/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3283 - accuracy: 0.9146 - val_loss: 1.8602 - val_accuracy: 0.7278 Epoch 151/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1388 - accuracy: 0.9573 - val_loss: 1.9389 - val_accuracy: 0.7028 Epoch 152/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0572 - accuracy: 0.9893 - val_loss: 2.5810 - val_accuracy: 0.7028 Epoch 153/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0338 - accuracy: 0.9881 - val_loss: 2.5342 - val_accuracy: 0.7100 Epoch 154/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0251 - accuracy: 0.9953 - val_loss: 2.4494 - val_accuracy: 0.7295 Epoch 155/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0410 - accuracy: 0.9929 - val_loss: 2.5263 - val_accuracy: 0.7224 Epoch 156/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0232 - accuracy: 0.9929 - val_loss: 2.7234 - val_accuracy: 0.7206 Epoch 157/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0226 - accuracy: 0.9917 - val_loss: 2.7856 - val_accuracy: 0.7224 Epoch 158/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0108 - accuracy: 0.9964 - val_loss: 2.9120 - val_accuracy: 0.7278 Epoch 159/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0290 - accuracy: 0.9964 - val_loss: 2.8644 - val_accuracy: 0.7153 Epoch 160/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0101 - accuracy: 0.9988 - val_loss: 3.0894 - val_accuracy: 0.7171 Epoch 161/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0390 - accuracy: 0.9953 - val_loss: 2.7201 - val_accuracy: 0.7171 Epoch 162/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0074 - accuracy: 0.9988 - val_loss: 2.8819 - val_accuracy: 0.7135 Epoch 163/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 2.9555 - val_accuracy: 0.7100 Epoch 164/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0046 - accuracy: 0.9988 - val_loss: 2.9939 - val_accuracy: 0.7100 Epoch 165/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0090 - accuracy: 0.9976 - val_loss: 3.2253 - val_accuracy: 0.7135 Epoch 166/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 3.1989 - val_accuracy: 0.7171 Epoch 167/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 3.2011 - val_accuracy: 0.7278 Epoch 168/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0029 - accuracy: 0.9988 - val_loss: 3.2859 - val_accuracy: 0.7189 Epoch 169/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0057 - accuracy: 0.9988 - val_loss: 3.0473 - val_accuracy: 0.7064 Epoch 170/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1165 - accuracy: 0.9763 - val_loss: 3.4154 - val_accuracy: 0.6762 Epoch 171/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2350 - accuracy: 0.9431 - val_loss: 2.6813 - val_accuracy: 0.6673 Epoch 172/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1695 - accuracy: 0.9585 - val_loss: 1.9494 - val_accuracy: 0.7473 Epoch 173/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0763 - accuracy: 0.9810 - val_loss: 2.1570 - val_accuracy: 0.7260 Epoch 174/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0386 - accuracy: 0.9953 - val_loss: 2.3837 - val_accuracy: 0.7295 Epoch 175/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0557 - accuracy: 0.9870 - val_loss: 2.3838 - val_accuracy: 0.7100 Epoch 176/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1462 - accuracy: 0.9632 - val_loss: 2.2428 - val_accuracy: 0.7153 Epoch 177/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0475 - accuracy: 0.9881 - val_loss: 2.2247 - val_accuracy: 0.7331 Epoch 178/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0421 - accuracy: 0.9929 - val_loss: 2.6587 - val_accuracy: 0.7064 Epoch 179/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0207 - accuracy: 0.9929 - val_loss: 2.5285 - val_accuracy: 0.7367 Epoch 180/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0338 - accuracy: 0.9905 - val_loss: 2.6069 - val_accuracy: 0.7206 Epoch 181/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0489 - accuracy: 0.9858 - val_loss: 2.5485 - val_accuracy: 0.7242 Epoch 182/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0949 - accuracy: 0.9763 - val_loss: 2.6366 - val_accuracy: 0.7242 Epoch 183/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0983 - accuracy: 0.9846 - val_loss: 2.2125 - val_accuracy: 0.7260 Epoch 184/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0481 - accuracy: 0.9870 - val_loss: 2.6907 - val_accuracy: 0.7064 Epoch 185/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0689 - accuracy: 0.9870 - val_loss: 2.7806 - val_accuracy: 0.6779 Epoch 186/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1118 - accuracy: 0.9703 - val_loss: 2.4077 - val_accuracy: 0.7224 Epoch 187/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0915 - accuracy: 0.9775 - val_loss: 2.1227 - val_accuracy: 0.7206 Epoch 188/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0432 - accuracy: 0.9870 - val_loss: 2.1678 - val_accuracy: 0.7224 Epoch 189/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0260 - accuracy: 0.9917 - val_loss: 2.2623 - val_accuracy: 0.7349 Epoch 190/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0166 - accuracy: 0.9964 - val_loss: 2.7294 - val_accuracy: 0.7278 Epoch 191/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0478 - accuracy: 0.9846 - val_loss: 2.6692 - val_accuracy: 0.7313 Epoch 192/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1453 - accuracy: 0.9727 - val_loss: 3.1524 - val_accuracy: 0.6548 Epoch 193/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0722 - accuracy: 0.9870 - val_loss: 2.4376 - val_accuracy: 0.7046 Epoch 194/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0194 - accuracy: 0.9988 - val_loss: 2.5307 - val_accuracy: 0.7189 Epoch 195/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0146 - accuracy: 0.9976 - val_loss: 2.8084 - val_accuracy: 0.7117 Epoch 196/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0112 - accuracy: 0.9964 - val_loss: 2.9439 - val_accuracy: 0.7367 Epoch 197/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0101 - accuracy: 0.9964 - val_loss: 2.6865 - val_accuracy: 0.7331 Epoch 198/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0050 - accuracy: 0.9988 - val_loss: 2.6923 - val_accuracy: 0.7367 Epoch 199/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0078 - accuracy: 0.9988 - val_loss: 2.7639 - val_accuracy: 0.7295 Epoch 200/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0161 - accuracy: 0.9964 - val_loss: 2.8118 - val_accuracy: 0.7313 Epoch 201/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 2.9268 - val_accuracy: 0.7313 Epoch 202/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 2.9835 - val_accuracy: 0.7260 Epoch 203/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0095 - accuracy: 0.9988 - val_loss: 2.9774 - val_accuracy: 0.7331 Epoch 204/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 3.0556 - val_accuracy: 0.7331 Epoch 205/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 3.1025 - val_accuracy: 0.7260 Epoch 206/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 3.1800 - val_accuracy: 0.7260 Epoch 207/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0058 - accuracy: 0.9976 - val_loss: 3.3378 - val_accuracy: 0.7189 Epoch 208/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 0.9988 - val_loss: 3.2910 - val_accuracy: 0.7224 Epoch 209/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0150 - accuracy: 0.9964 - val_loss: 3.3976 - val_accuracy: 0.7224 Epoch 210/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2012 - accuracy: 0.9609 - val_loss: 3.0926 - val_accuracy: 0.6601 Epoch 211/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1765 - accuracy: 0.9526 - val_loss: 2.0000 - val_accuracy: 0.6993 Epoch 212/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1373 - accuracy: 0.9620 - val_loss: 2.1702 - val_accuracy: 0.7028 Epoch 213/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0453 - accuracy: 0.9929 - val_loss: 2.1698 - val_accuracy: 0.7367 Epoch 214/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0121 - accuracy: 0.9988 - val_loss: 2.4070 - val_accuracy: 0.7402 Epoch 215/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 2.5191 - val_accuracy: 0.7402 Epoch 216/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 2.6325 - val_accuracy: 0.7367 Epoch 217/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 2.7147 - val_accuracy: 0.7402 Epoch 218/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 2.7756 - val_accuracy: 0.7438 Epoch 219/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 2.6979 - val_accuracy: 0.7473 Epoch 220/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0115 - accuracy: 0.9988 - val_loss: 2.7965 - val_accuracy: 0.7402 Epoch 221/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 2.8646 - val_accuracy: 0.7420 Epoch 222/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 2.9624 - val_accuracy: 0.7420 Epoch 223/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0029 - accuracy: 0.9988 - val_loss: 3.0109 - val_accuracy: 0.7473 Epoch 224/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 3.0605 - val_accuracy: 0.7438 Epoch 225/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 3.0902 - val_accuracy: 0.7456 Epoch 226/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 3.1155 - val_accuracy: 0.7420 Epoch 227/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0026 - accuracy: 0.9988 - val_loss: 3.1561 - val_accuracy: 0.7331 Epoch 228/750 27/27 [==============================] - 0s 2ms/step - loss: 6.1160e-04 - accuracy: 1.0000 - val_loss: 3.1875 - val_accuracy: 0.7278 Epoch 229/750 27/27 [==============================] - 0s 2ms/step - loss: 9.0775e-04 - accuracy: 1.0000 - val_loss: 3.2238 - val_accuracy: 0.7295 Epoch 230/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 3.2862 - val_accuracy: 0.7313 Epoch 231/750 27/27 [==============================] - 0s 2ms/step - loss: 5.4172e-04 - accuracy: 1.0000 - val_loss: 3.3041 - val_accuracy: 0.7367 Epoch 232/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 3.3559 - val_accuracy: 0.7331 Epoch 233/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0124 - accuracy: 0.9988 - val_loss: 3.4165 - val_accuracy: 0.7313 Epoch 234/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0099 - accuracy: 0.9976 - val_loss: 3.5069 - val_accuracy: 0.7242 Epoch 235/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0109 - accuracy: 0.9953 - val_loss: 3.5610 - val_accuracy: 0.7224 Epoch 236/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0936 - accuracy: 0.9798 - val_loss: 3.2404 - val_accuracy: 0.7100 Epoch 237/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3034 - accuracy: 0.9336 - val_loss: 1.8618 - val_accuracy: 0.7100 Epoch 238/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2282 - accuracy: 0.9265 - val_loss: 2.1351 - val_accuracy: 0.7384 Epoch 239/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0931 - accuracy: 0.9751 - val_loss: 2.1132 - val_accuracy: 0.7331 Epoch 240/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1588 - accuracy: 0.9620 - val_loss: 2.2224 - val_accuracy: 0.7064 Epoch 241/750 27/27 [==============================] - 0s 3ms/step - loss: 0.1958 - accuracy: 0.9490 - val_loss: 2.1821 - val_accuracy: 0.7206 Epoch 242/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0639 - accuracy: 0.9822 - val_loss: 2.3030 - val_accuracy: 0.7278 Epoch 243/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0190 - accuracy: 0.9953 - val_loss: 2.6708 - val_accuracy: 0.7189 Epoch 244/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0699 - accuracy: 0.9846 - val_loss: 2.4602 - val_accuracy: 0.7224 Epoch 245/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0302 - accuracy: 0.9905 - val_loss: 2.4979 - val_accuracy: 0.7384 Epoch 246/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0325 - accuracy: 0.9905 - val_loss: 2.7825 - val_accuracy: 0.7295 Epoch 247/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2858 - accuracy: 0.9537 - val_loss: 1.6419 - val_accuracy: 0.6940 Epoch 248/750 27/27 [==============================] - 0s 3ms/step - loss: 0.1959 - accuracy: 0.9502 - val_loss: 1.8806 - val_accuracy: 0.7011 Epoch 249/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0531 - accuracy: 0.9786 - val_loss: 2.1255 - val_accuracy: 0.7189 Epoch 250/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0265 - accuracy: 0.9893 - val_loss: 2.1904 - val_accuracy: 0.7313 Epoch 251/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0135 - accuracy: 0.9964 - val_loss: 2.2953 - val_accuracy: 0.7313 Epoch 252/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 2.3132 - val_accuracy: 0.7367 Epoch 253/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 2.3438 - val_accuracy: 0.7402 Epoch 254/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 2.4511 - val_accuracy: 0.7349 Epoch 255/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 2.5183 - val_accuracy: 0.7295 Epoch 256/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 2.5362 - val_accuracy: 0.7420 Epoch 257/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 2.6393 - val_accuracy: 0.7349 Epoch 258/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 2.7032 - val_accuracy: 0.7402 Epoch 259/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 2.7364 - val_accuracy: 0.7367 Epoch 260/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0026 - accuracy: 0.9988 - val_loss: 2.7214 - val_accuracy: 0.7367 Epoch 261/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 2.8116 - val_accuracy: 0.7402 Epoch 262/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.8467 - val_accuracy: 0.7402 Epoch 263/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0028 - accuracy: 0.9988 - val_loss: 3.0192 - val_accuracy: 0.7367 Epoch 264/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.9273 - val_accuracy: 0.7384 Epoch 265/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 2.9375 - val_accuracy: 0.7384 Epoch 266/750 27/27 [==============================] - 0s 3ms/step - loss: 6.2781e-04 - accuracy: 1.0000 - val_loss: 3.0661 - val_accuracy: 0.7331 Epoch 267/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 3.1646 - val_accuracy: 0.7278 Epoch 268/750 27/27 [==============================] - 0s 3ms/step - loss: 6.3274e-04 - accuracy: 1.0000 - val_loss: 3.1936 - val_accuracy: 0.7278 Epoch 269/750 27/27 [==============================] - 0s 3ms/step - loss: 7.6470e-04 - accuracy: 1.0000 - val_loss: 3.2320 - val_accuracy: 0.7278 Epoch 270/750 27/27 [==============================] - 0s 3ms/step - loss: 4.4165e-04 - accuracy: 1.0000 - val_loss: 3.2517 - val_accuracy: 0.7260 Epoch 271/750 27/27 [==============================] - 0s 3ms/step - loss: 6.6438e-04 - accuracy: 1.0000 - val_loss: 3.3285 - val_accuracy: 0.7295 Epoch 272/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0019 - accuracy: 0.9988 - val_loss: 3.3192 - val_accuracy: 0.7278 Epoch 273/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 3.3485 - val_accuracy: 0.7295 Epoch 274/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0075 - accuracy: 0.9988 - val_loss: 3.3331 - val_accuracy: 0.7456 Epoch 275/750 27/27 [==============================] - 0s 3ms/step - loss: 0.2357 - accuracy: 0.9609 - val_loss: 2.0632 - val_accuracy: 0.7260 Epoch 276/750 27/27 [==============================] - 0s 3ms/step - loss: 0.1214 - accuracy: 0.9656 - val_loss: 2.1854 - val_accuracy: 0.7260 Epoch 277/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0763 - accuracy: 0.9846 - val_loss: 2.0521 - val_accuracy: 0.7189 Epoch 278/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2339 - accuracy: 0.9537 - val_loss: 2.4165 - val_accuracy: 0.6815 Epoch 279/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2282 - accuracy: 0.9514 - val_loss: 1.8125 - val_accuracy: 0.7011 Epoch 280/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1024 - accuracy: 0.9798 - val_loss: 1.6958 - val_accuracy: 0.7295 Epoch 281/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0230 - accuracy: 0.9976 - val_loss: 1.9848 - val_accuracy: 0.7402 Epoch 282/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0361 - accuracy: 0.9929 - val_loss: 2.3473 - val_accuracy: 0.7046 Epoch 283/750 27/27 [==============================] - 0s 3ms/step - loss: 0.1309 - accuracy: 0.9715 - val_loss: 2.1190 - val_accuracy: 0.7117 Epoch 284/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0704 - accuracy: 0.9858 - val_loss: 1.8809 - val_accuracy: 0.7260 Epoch 285/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0536 - accuracy: 0.9822 - val_loss: 2.4380 - val_accuracy: 0.7189 Epoch 286/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0221 - accuracy: 0.9953 - val_loss: 2.5926 - val_accuracy: 0.7117 Epoch 287/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0132 - accuracy: 0.9953 - val_loss: 2.5015 - val_accuracy: 0.7242 Epoch 288/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0073 - accuracy: 0.9988 - val_loss: 2.5928 - val_accuracy: 0.7295 Epoch 289/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 2.7265 - val_accuracy: 0.7260 Epoch 290/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 2.7759 - val_accuracy: 0.7224 Epoch 291/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 2.8071 - val_accuracy: 0.7224 Epoch 292/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.8511 - val_accuracy: 0.7278 Epoch 293/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0117 - accuracy: 0.9976 - val_loss: 2.9226 - val_accuracy: 0.7278 Epoch 294/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 2.8455 - val_accuracy: 0.7349 Epoch 295/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0055 - accuracy: 0.9988 - val_loss: 2.8935 - val_accuracy: 0.7367 Epoch 296/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 3.0042 - val_accuracy: 0.7278 Epoch 297/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 3.0294 - val_accuracy: 0.7349 Epoch 298/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0020 - accuracy: 0.9988 - val_loss: 2.9778 - val_accuracy: 0.7402 Epoch 299/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 3.0298 - val_accuracy: 0.7384 Epoch 300/750 27/27 [==============================] - 0s 3ms/step - loss: 6.4037e-04 - accuracy: 1.0000 - val_loss: 3.0697 - val_accuracy: 0.7384 Epoch 301/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 3.1421 - val_accuracy: 0.7367 Epoch 302/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0022 - accuracy: 0.9988 - val_loss: 3.1451 - val_accuracy: 0.7331 Epoch 303/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0169 - accuracy: 0.9976 - val_loss: 3.1826 - val_accuracy: 0.7420 Epoch 304/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0070 - accuracy: 0.9976 - val_loss: 3.3966 - val_accuracy: 0.7242 Epoch 305/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0178 - accuracy: 0.9953 - val_loss: 3.2664 - val_accuracy: 0.7189 Epoch 306/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0098 - accuracy: 0.9964 - val_loss: 3.8920 - val_accuracy: 0.6993 Epoch 307/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0451 - accuracy: 0.9858 - val_loss: 3.6572 - val_accuracy: 0.7011 Epoch 308/750 27/27 [==============================] - 0s 2ms/step - loss: 0.3524 - accuracy: 0.9359 - val_loss: 1.9492 - val_accuracy: 0.7028 Epoch 309/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1948 - accuracy: 0.9561 - val_loss: 1.5438 - val_accuracy: 0.7224 Epoch 310/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1089 - accuracy: 0.9727 - val_loss: 1.7426 - val_accuracy: 0.7117 Epoch 311/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0300 - accuracy: 0.9929 - val_loss: 2.1965 - val_accuracy: 0.7064 Epoch 312/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0184 - accuracy: 0.9953 - val_loss: 2.4218 - val_accuracy: 0.7242 Epoch 313/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0939 - accuracy: 0.9822 - val_loss: 2.3083 - val_accuracy: 0.7082 Epoch 314/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0419 - accuracy: 0.9893 - val_loss: 2.2961 - val_accuracy: 0.7064 Epoch 315/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0160 - accuracy: 0.9976 - val_loss: 2.0570 - val_accuracy: 0.7117 Epoch 316/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0290 - accuracy: 0.9929 - val_loss: 2.4621 - val_accuracy: 0.7189 Epoch 317/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0453 - accuracy: 0.9893 - val_loss: 2.2261 - val_accuracy: 0.7171 Epoch 318/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0834 - accuracy: 0.9917 - val_loss: 2.3347 - val_accuracy: 0.7313 Epoch 319/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0265 - accuracy: 0.9929 - val_loss: 2.2477 - val_accuracy: 0.7367 Epoch 320/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0095 - accuracy: 0.9988 - val_loss: 2.2652 - val_accuracy: 0.7331 Epoch 321/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0179 - accuracy: 0.9953 - val_loss: 2.2946 - val_accuracy: 0.7313 Epoch 322/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0351 - accuracy: 0.9964 - val_loss: 2.3923 - val_accuracy: 0.7028 Epoch 323/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0718 - accuracy: 0.9858 - val_loss: 2.0942 - val_accuracy: 0.7153 Epoch 324/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0439 - accuracy: 0.9893 - val_loss: 2.1651 - val_accuracy: 0.7331 Epoch 325/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0233 - accuracy: 0.9988 - val_loss: 2.0932 - val_accuracy: 0.7278 Epoch 326/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 2.0903 - val_accuracy: 0.7349 Epoch 327/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 2.2155 - val_accuracy: 0.7278 Epoch 328/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0288 - accuracy: 0.9941 - val_loss: 2.1931 - val_accuracy: 0.7242 Epoch 329/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0108 - accuracy: 0.9988 - val_loss: 2.2513 - val_accuracy: 0.7402 Epoch 330/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0527 - accuracy: 0.9917 - val_loss: 2.6145 - val_accuracy: 0.7242 Epoch 331/750 27/27 [==============================] - 0s 3ms/step - loss: 0.1983 - accuracy: 0.9514 - val_loss: 2.2268 - val_accuracy: 0.6744 Epoch 332/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0934 - accuracy: 0.9763 - val_loss: 1.8926 - val_accuracy: 0.7242 Epoch 333/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0493 - accuracy: 0.9893 - val_loss: 1.9345 - val_accuracy: 0.7278 Epoch 334/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0203 - accuracy: 0.9941 - val_loss: 2.1532 - val_accuracy: 0.7171 Epoch 335/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0064 - accuracy: 0.9988 - val_loss: 2.3445 - val_accuracy: 0.7295 Epoch 336/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 2.3886 - val_accuracy: 0.7295 Epoch 337/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0059 - accuracy: 0.9988 - val_loss: 2.4480 - val_accuracy: 0.7295 Epoch 338/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 2.5372 - val_accuracy: 0.7313 Epoch 339/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 2.5822 - val_accuracy: 0.7295 Epoch 340/750 27/27 [==============================] - 0s 2ms/step - loss: 9.2874e-04 - accuracy: 1.0000 - val_loss: 2.6177 - val_accuracy: 0.7295 Epoch 341/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 2.6524 - val_accuracy: 0.7313 Epoch 342/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 2.7518 - val_accuracy: 0.7064 Epoch 343/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0102 - accuracy: 0.9976 - val_loss: 2.9018 - val_accuracy: 0.7367 Epoch 344/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1449 - accuracy: 0.9775 - val_loss: 2.1636 - val_accuracy: 0.7153 Epoch 345/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1822 - accuracy: 0.9442 - val_loss: 1.9320 - val_accuracy: 0.7260 Epoch 346/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0458 - accuracy: 0.9905 - val_loss: 1.9608 - val_accuracy: 0.7224 Epoch 347/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0291 - accuracy: 0.9929 - val_loss: 2.3290 - val_accuracy: 0.7135 Epoch 348/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0138 - accuracy: 0.9976 - val_loss: 2.3038 - val_accuracy: 0.7278 Epoch 349/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9988 - val_loss: 2.4651 - val_accuracy: 0.7171 Epoch 350/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 2.4884 - val_accuracy: 0.7260 Epoch 351/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 2.5193 - val_accuracy: 0.7153 Epoch 352/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 2.4611 - val_accuracy: 0.7260 Epoch 353/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.4986 - val_accuracy: 0.7313 Epoch 354/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 2.5680 - val_accuracy: 0.7349 Epoch 355/750 27/27 [==============================] - 0s 2ms/step - loss: 8.0919e-04 - accuracy: 1.0000 - val_loss: 2.6006 - val_accuracy: 0.7331 Epoch 356/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 2.6304 - val_accuracy: 0.7331 Epoch 357/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0644 - accuracy: 0.9881 - val_loss: 3.2845 - val_accuracy: 0.6904 Epoch 358/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2044 - accuracy: 0.9502 - val_loss: 1.8251 - val_accuracy: 0.7082 Epoch 359/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0935 - accuracy: 0.9798 - val_loss: 1.7941 - val_accuracy: 0.7491 Epoch 360/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0958 - accuracy: 0.9763 - val_loss: 2.4638 - val_accuracy: 0.7206 Epoch 361/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1049 - accuracy: 0.9739 - val_loss: 1.7025 - val_accuracy: 0.7313 Epoch 362/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0350 - accuracy: 0.9941 - val_loss: 2.1756 - val_accuracy: 0.7295 Epoch 363/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0647 - accuracy: 0.9870 - val_loss: 1.9901 - val_accuracy: 0.7153 Epoch 364/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0178 - accuracy: 0.9976 - val_loss: 2.1253 - val_accuracy: 0.7420 Epoch 365/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0097 - accuracy: 0.9988 - val_loss: 2.1633 - val_accuracy: 0.7260 Epoch 366/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0045 - accuracy: 0.9988 - val_loss: 2.2702 - val_accuracy: 0.7438 Epoch 367/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 2.3735 - val_accuracy: 0.7473 Epoch 368/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0030 - accuracy: 0.9988 - val_loss: 2.4477 - val_accuracy: 0.7420 Epoch 369/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.4936 - val_accuracy: 0.7367 Epoch 370/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0046 - accuracy: 0.9988 - val_loss: 2.5592 - val_accuracy: 0.7313 Epoch 371/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 2.6183 - val_accuracy: 0.7313 Epoch 372/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.6629 - val_accuracy: 0.7313 Epoch 373/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.6935 - val_accuracy: 0.7295 Epoch 374/750 27/27 [==============================] - 0s 2ms/step - loss: 8.1192e-04 - accuracy: 1.0000 - val_loss: 2.7226 - val_accuracy: 0.7295 Epoch 375/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.7517 - val_accuracy: 0.7313 Epoch 376/750 27/27 [==============================] - 0s 2ms/step - loss: 8.3450e-04 - accuracy: 1.0000 - val_loss: 2.7732 - val_accuracy: 0.7331 Epoch 377/750 27/27 [==============================] - 0s 2ms/step - loss: 4.7015e-04 - accuracy: 1.0000 - val_loss: 2.8098 - val_accuracy: 0.7313 Epoch 378/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 2.8417 - val_accuracy: 0.7278 Epoch 379/750 27/27 [==============================] - 0s 2ms/step - loss: 4.0949e-04 - accuracy: 1.0000 - val_loss: 2.8228 - val_accuracy: 0.7278 Epoch 380/750 27/27 [==============================] - 0s 3ms/step - loss: 5.2127e-04 - accuracy: 1.0000 - val_loss: 2.8552 - val_accuracy: 0.7278 Epoch 381/750 27/27 [==============================] - 0s 2ms/step - loss: 8.1901e-04 - accuracy: 1.0000 - val_loss: 2.8819 - val_accuracy: 0.7278 Epoch 382/750 27/27 [==============================] - 0s 2ms/step - loss: 6.1702e-04 - accuracy: 1.0000 - val_loss: 2.9025 - val_accuracy: 0.7313 Epoch 383/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 2.9432 - val_accuracy: 0.7313 Epoch 384/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0035 - accuracy: 0.9988 - val_loss: 3.0012 - val_accuracy: 0.7313 Epoch 385/750 27/27 [==============================] - 0s 2ms/step - loss: 6.6587e-04 - accuracy: 1.0000 - val_loss: 3.0291 - val_accuracy: 0.7384 Epoch 386/750 27/27 [==============================] - 0s 2ms/step - loss: 1.9959e-04 - accuracy: 1.0000 - val_loss: 3.0482 - val_accuracy: 0.7402 Epoch 387/750 27/27 [==============================] - 0s 2ms/step - loss: 1.9021e-04 - accuracy: 1.0000 - val_loss: 3.0567 - val_accuracy: 0.7402 Epoch 388/750 27/27 [==============================] - 0s 2ms/step - loss: 4.5242e-04 - accuracy: 1.0000 - val_loss: 3.0820 - val_accuracy: 0.7402 Epoch 389/750 27/27 [==============================] - 0s 2ms/step - loss: 3.1029e-04 - accuracy: 1.0000 - val_loss: 3.0935 - val_accuracy: 0.7384 Epoch 390/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0029 - accuracy: 0.9988 - val_loss: 3.1241 - val_accuracy: 0.7367 Epoch 391/750 27/27 [==============================] - 0s 2ms/step - loss: 5.0755e-04 - accuracy: 1.0000 - val_loss: 3.1338 - val_accuracy: 0.7384 Epoch 392/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 3.1766 - val_accuracy: 0.7349 Epoch 393/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9988 - val_loss: 3.1709 - val_accuracy: 0.7402 Epoch 394/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 3.1893 - val_accuracy: 0.7384 Epoch 395/750 27/27 [==============================] - 0s 2ms/step - loss: 1.7208e-04 - accuracy: 1.0000 - val_loss: 3.2080 - val_accuracy: 0.7367 Epoch 396/750 27/27 [==============================] - 0s 2ms/step - loss: 4.3117e-04 - accuracy: 1.0000 - val_loss: 3.1995 - val_accuracy: 0.7384 Epoch 397/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0026 - accuracy: 0.9988 - val_loss: 3.2498 - val_accuracy: 0.7331 Epoch 398/750 27/27 [==============================] - 0s 2ms/step - loss: 2.3678e-04 - accuracy: 1.0000 - val_loss: 3.2531 - val_accuracy: 0.7349 Epoch 399/750 27/27 [==============================] - 0s 2ms/step - loss: 8.8592e-04 - accuracy: 1.0000 - val_loss: 3.3422 - val_accuracy: 0.7260 Epoch 400/750 27/27 [==============================] - 0s 2ms/step - loss: 9.3238e-05 - accuracy: 1.0000 - val_loss: 3.3707 - val_accuracy: 0.7295 Epoch 401/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9988 - val_loss: 3.3808 - val_accuracy: 0.7295 Epoch 402/750 27/27 [==============================] - 0s 2ms/step - loss: 2.4233e-04 - accuracy: 1.0000 - val_loss: 3.3924 - val_accuracy: 0.7313 Epoch 403/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0055 - accuracy: 0.9964 - val_loss: 3.3275 - val_accuracy: 0.7278 Epoch 404/750 27/27 [==============================] - 0s 2ms/step - loss: 7.6027e-04 - accuracy: 1.0000 - val_loss: 3.3250 - val_accuracy: 0.7242 Epoch 405/750 27/27 [==============================] - 0s 2ms/step - loss: 1.1993e-04 - accuracy: 1.0000 - val_loss: 3.3155 - val_accuracy: 0.7278 Epoch 406/750 27/27 [==============================] - 0s 2ms/step - loss: 1.3929e-04 - accuracy: 1.0000 - val_loss: 3.3241 - val_accuracy: 0.7295 Epoch 407/750 27/27 [==============================] - 0s 2ms/step - loss: 3.5348e-04 - accuracy: 1.0000 - val_loss: 3.3580 - val_accuracy: 0.7295 Epoch 408/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 0.9988 - val_loss: 3.4026 - val_accuracy: 0.7260 Epoch 409/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1946 - accuracy: 0.9727 - val_loss: 2.7092 - val_accuracy: 0.6726 Epoch 410/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1217 - accuracy: 0.9668 - val_loss: 2.0425 - val_accuracy: 0.7295 Epoch 411/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0771 - accuracy: 0.9798 - val_loss: 2.3131 - val_accuracy: 0.6851 Epoch 412/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1367 - accuracy: 0.9668 - val_loss: 2.4093 - val_accuracy: 0.6851 Epoch 413/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0572 - accuracy: 0.9846 - val_loss: 2.0193 - val_accuracy: 0.7242 Epoch 414/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0405 - accuracy: 0.9893 - val_loss: 1.9492 - val_accuracy: 0.7295 Epoch 415/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0120 - accuracy: 0.9964 - val_loss: 2.1822 - val_accuracy: 0.7313 Epoch 416/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 2.4373 - val_accuracy: 0.7242 Epoch 417/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0510 - accuracy: 0.9893 - val_loss: 2.3273 - val_accuracy: 0.7135 Epoch 418/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0519 - accuracy: 0.9893 - val_loss: 2.1818 - val_accuracy: 0.7313 Epoch 419/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 2.1095 - val_accuracy: 0.7278 Epoch 420/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0090 - accuracy: 0.9976 - val_loss: 2.4219 - val_accuracy: 0.7313 Epoch 421/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0119 - accuracy: 0.9964 - val_loss: 2.3927 - val_accuracy: 0.7349 Epoch 422/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0084 - accuracy: 0.9988 - val_loss: 2.4085 - val_accuracy: 0.7367 Epoch 423/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 2.5279 - val_accuracy: 0.7313 Epoch 424/750 27/27 [==============================] - 0s 2ms/step - loss: 5.9919e-04 - accuracy: 1.0000 - val_loss: 2.5602 - val_accuracy: 0.7313 Epoch 425/750 27/27 [==============================] - 0s 2ms/step - loss: 8.8199e-04 - accuracy: 1.0000 - val_loss: 2.5571 - val_accuracy: 0.7384 Epoch 426/750 27/27 [==============================] - 0s 2ms/step - loss: 7.2639e-04 - accuracy: 1.0000 - val_loss: 2.5963 - val_accuracy: 0.7384 Epoch 427/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 2.7358 - val_accuracy: 0.7349 Epoch 428/750 27/27 [==============================] - 0s 2ms/step - loss: 5.4457e-04 - accuracy: 1.0000 - val_loss: 2.7915 - val_accuracy: 0.7367 Epoch 429/750 27/27 [==============================] - 0s 2ms/step - loss: 5.2305e-04 - accuracy: 1.0000 - val_loss: 2.8235 - val_accuracy: 0.7402 Epoch 430/750 27/27 [==============================] - 0s 2ms/step - loss: 4.8380e-04 - accuracy: 1.0000 - val_loss: 2.8465 - val_accuracy: 0.7420 Epoch 431/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 2.8575 - val_accuracy: 0.7384 Epoch 432/750 27/27 [==============================] - 0s 2ms/step - loss: 2.2673e-04 - accuracy: 1.0000 - val_loss: 2.8802 - val_accuracy: 0.7384 Epoch 433/750 27/27 [==============================] - 0s 2ms/step - loss: 4.9437e-04 - accuracy: 1.0000 - val_loss: 2.9089 - val_accuracy: 0.7402 Epoch 434/750 27/27 [==============================] - 0s 2ms/step - loss: 3.4534e-04 - accuracy: 1.0000 - val_loss: 2.9451 - val_accuracy: 0.7420 Epoch 435/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 2.9772 - val_accuracy: 0.7402 Epoch 436/750 27/27 [==============================] - 0s 2ms/step - loss: 5.1711e-04 - accuracy: 1.0000 - val_loss: 2.9826 - val_accuracy: 0.7438 Epoch 437/750 27/27 [==============================] - 0s 2ms/step - loss: 3.4997e-04 - accuracy: 1.0000 - val_loss: 3.0134 - val_accuracy: 0.7438 Epoch 438/750 27/27 [==============================] - 0s 2ms/step - loss: 7.7957e-04 - accuracy: 1.0000 - val_loss: 3.1107 - val_accuracy: 0.7473 Epoch 439/750 27/27 [==============================] - 0s 2ms/step - loss: 2.1987e-04 - accuracy: 1.0000 - val_loss: 3.1170 - val_accuracy: 0.7438 Epoch 440/750 27/27 [==============================] - 0s 2ms/step - loss: 1.9475e-04 - accuracy: 1.0000 - val_loss: 3.1192 - val_accuracy: 0.7438 Epoch 441/750 27/27 [==============================] - 0s 2ms/step - loss: 6.8207e-04 - accuracy: 1.0000 - val_loss: 3.1791 - val_accuracy: 0.7456 Epoch 442/750 27/27 [==============================] - 0s 2ms/step - loss: 4.5976e-04 - accuracy: 1.0000 - val_loss: 3.2098 - val_accuracy: 0.7473 Epoch 443/750 27/27 [==============================] - 0s 2ms/step - loss: 1.2802e-04 - accuracy: 1.0000 - val_loss: 3.2216 - val_accuracy: 0.7456 Epoch 444/750 27/27 [==============================] - 0s 2ms/step - loss: 5.0311e-04 - accuracy: 1.0000 - val_loss: 3.1958 - val_accuracy: 0.7402 Epoch 445/750 27/27 [==============================] - 0s 2ms/step - loss: 1.7676e-04 - accuracy: 1.0000 - val_loss: 3.2110 - val_accuracy: 0.7402 Epoch 446/750 27/27 [==============================] - 0s 2ms/step - loss: 9.6274e-05 - accuracy: 1.0000 - val_loss: 3.2267 - val_accuracy: 0.7402 Epoch 447/750 27/27 [==============================] - 0s 2ms/step - loss: 3.6923e-04 - accuracy: 1.0000 - val_loss: 3.2445 - val_accuracy: 0.7456 Epoch 448/750 27/27 [==============================] - 0s 2ms/step - loss: 2.3819e-04 - accuracy: 1.0000 - val_loss: 3.2771 - val_accuracy: 0.7491 Epoch 449/750 27/27 [==============================] - 0s 2ms/step - loss: 5.4908e-04 - accuracy: 1.0000 - val_loss: 3.2572 - val_accuracy: 0.7491 Epoch 450/750 27/27 [==============================] - 0s 2ms/step - loss: 1.4316e-04 - accuracy: 1.0000 - val_loss: 3.2139 - val_accuracy: 0.7491 Epoch 451/750 27/27 [==============================] - 0s 2ms/step - loss: 2.3990e-04 - accuracy: 1.0000 - val_loss: 3.2421 - val_accuracy: 0.7509 Epoch 452/750 27/27 [==============================] - 0s 2ms/step - loss: 2.0703e-04 - accuracy: 1.0000 - val_loss: 3.2883 - val_accuracy: 0.7509 Epoch 453/750 27/27 [==============================] - 0s 2ms/step - loss: 9.8184e-04 - accuracy: 1.0000 - val_loss: 3.2861 - val_accuracy: 0.7509 Epoch 454/750 27/27 [==============================] - 0s 2ms/step - loss: 2.0253e-04 - accuracy: 1.0000 - val_loss: 3.2935 - val_accuracy: 0.7509 Epoch 455/750 27/27 [==============================] - 0s 2ms/step - loss: 3.7305e-04 - accuracy: 1.0000 - val_loss: 3.3186 - val_accuracy: 0.7473 Epoch 456/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 0.9988 - val_loss: 3.5002 - val_accuracy: 0.7473 Epoch 457/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1906 - accuracy: 0.9692 - val_loss: 2.1768 - val_accuracy: 0.7260 Epoch 458/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2007 - accuracy: 0.9537 - val_loss: 2.0831 - val_accuracy: 0.7206 Epoch 459/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1256 - accuracy: 0.9632 - val_loss: 1.9294 - val_accuracy: 0.6993 Epoch 460/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0881 - accuracy: 0.9775 - val_loss: 2.4835 - val_accuracy: 0.6922 Epoch 461/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0671 - accuracy: 0.9822 - val_loss: 1.8677 - val_accuracy: 0.7456 Epoch 462/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0522 - accuracy: 0.9893 - val_loss: 2.4362 - val_accuracy: 0.7135 Epoch 463/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0588 - accuracy: 0.9846 - val_loss: 3.2568 - val_accuracy: 0.7011 Epoch 464/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1181 - accuracy: 0.9715 - val_loss: 2.1625 - val_accuracy: 0.7224 Epoch 465/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0899 - accuracy: 0.9810 - val_loss: 2.2972 - val_accuracy: 0.7153 Epoch 466/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0996 - accuracy: 0.9775 - val_loss: 1.9900 - val_accuracy: 0.7278 Epoch 467/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1817 - accuracy: 0.9597 - val_loss: 1.5655 - val_accuracy: 0.7260 Epoch 468/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0649 - accuracy: 0.9905 - val_loss: 1.9271 - val_accuracy: 0.7224 Epoch 469/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0202 - accuracy: 0.9953 - val_loss: 2.2055 - val_accuracy: 0.7100 Epoch 470/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0093 - accuracy: 0.9988 - val_loss: 2.3275 - val_accuracy: 0.7260 Epoch 471/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 2.4754 - val_accuracy: 0.7313 Epoch 472/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 2.5562 - val_accuracy: 0.7189 Epoch 473/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 2.6200 - val_accuracy: 0.7206 Epoch 474/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.6647 - val_accuracy: 0.7242 Epoch 475/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 2.6885 - val_accuracy: 0.7260 Epoch 476/750 27/27 [==============================] - 0s 2ms/step - loss: 9.5887e-04 - accuracy: 1.0000 - val_loss: 2.7452 - val_accuracy: 0.7242 Epoch 477/750 27/27 [==============================] - 0s 2ms/step - loss: 5.6764e-04 - accuracy: 1.0000 - val_loss: 2.7647 - val_accuracy: 0.7278 Epoch 478/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.8167 - val_accuracy: 0.7278 Epoch 479/750 27/27 [==============================] - 0s 2ms/step - loss: 5.4480e-04 - accuracy: 1.0000 - val_loss: 2.8417 - val_accuracy: 0.7278 Epoch 480/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0071 - accuracy: 0.9988 - val_loss: 2.8767 - val_accuracy: 0.7278 Epoch 481/750 27/27 [==============================] - 0s 2ms/step - loss: 5.1902e-04 - accuracy: 1.0000 - val_loss: 2.9124 - val_accuracy: 0.7295 Epoch 482/750 27/27 [==============================] - 0s 2ms/step - loss: 5.4093e-04 - accuracy: 1.0000 - val_loss: 2.9220 - val_accuracy: 0.7278 Epoch 483/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0052 - accuracy: 0.9988 - val_loss: 2.9493 - val_accuracy: 0.7278 Epoch 484/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9976 - val_loss: 2.8079 - val_accuracy: 0.7313 Epoch 485/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0054 - accuracy: 0.9988 - val_loss: 2.7588 - val_accuracy: 0.7260 Epoch 486/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 0.9988 - val_loss: 2.8767 - val_accuracy: 0.7349 Epoch 487/750 27/27 [==============================] - 0s 2ms/step - loss: 5.1768e-04 - accuracy: 1.0000 - val_loss: 2.8904 - val_accuracy: 0.7313 Epoch 488/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.8768 - val_accuracy: 0.7367 Epoch 489/750 27/27 [==============================] - 0s 2ms/step - loss: 4.5027e-04 - accuracy: 1.0000 - val_loss: 2.9048 - val_accuracy: 0.7367 Epoch 490/750 27/27 [==============================] - 0s 2ms/step - loss: 6.5566e-04 - accuracy: 1.0000 - val_loss: 2.9476 - val_accuracy: 0.7367 Epoch 491/750 27/27 [==============================] - 0s 2ms/step - loss: 9.1103e-04 - accuracy: 1.0000 - val_loss: 3.1142 - val_accuracy: 0.7402 Epoch 492/750 27/27 [==============================] - 0s 2ms/step - loss: 9.0982e-04 - accuracy: 1.0000 - val_loss: 3.1184 - val_accuracy: 0.7420 Epoch 493/750 27/27 [==============================] - 0s 2ms/step - loss: 3.5075e-04 - accuracy: 1.0000 - val_loss: 3.1242 - val_accuracy: 0.7384 Epoch 494/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0025 - accuracy: 0.9988 - val_loss: 3.1825 - val_accuracy: 0.7420 Epoch 495/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 0.9988 - val_loss: 3.1851 - val_accuracy: 0.7295 Epoch 496/750 27/27 [==============================] - 0s 2ms/step - loss: 2.9922e-04 - accuracy: 1.0000 - val_loss: 3.2032 - val_accuracy: 0.7278 Epoch 497/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0048 - accuracy: 0.9988 - val_loss: 3.1402 - val_accuracy: 0.7349 Epoch 498/750 27/27 [==============================] - 0s 2ms/step - loss: 2.9670e-04 - accuracy: 1.0000 - val_loss: 3.1452 - val_accuracy: 0.7295 Epoch 499/750 27/27 [==============================] - 0s 2ms/step - loss: 9.2744e-04 - accuracy: 1.0000 - val_loss: 3.2212 - val_accuracy: 0.7278 Epoch 500/750 27/27 [==============================] - 0s 2ms/step - loss: 2.1650e-04 - accuracy: 1.0000 - val_loss: 3.2621 - val_accuracy: 0.7278 Epoch 501/750 27/27 [==============================] - 0s 2ms/step - loss: 7.5441e-04 - accuracy: 1.0000 - val_loss: 3.2924 - val_accuracy: 0.7313 Epoch 502/750 27/27 [==============================] - 0s 2ms/step - loss: 4.7112e-04 - accuracy: 1.0000 - val_loss: 3.2943 - val_accuracy: 0.7295 Epoch 503/750 27/27 [==============================] - 0s 2ms/step - loss: 2.7334e-04 - accuracy: 1.0000 - val_loss: 3.3257 - val_accuracy: 0.7278 Epoch 504/750 27/27 [==============================] - 0s 2ms/step - loss: 9.6338e-04 - accuracy: 1.0000 - val_loss: 3.4437 - val_accuracy: 0.7295 Epoch 505/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1227 - accuracy: 0.9870 - val_loss: 3.4647 - val_accuracy: 0.7100 Epoch 506/750 27/27 [==============================] - 0s 2ms/step - loss: 0.6849 - accuracy: 0.8529 - val_loss: 1.7578 - val_accuracy: 0.6317 Epoch 507/750 27/27 [==============================] - 0s 2ms/step - loss: 0.5149 - accuracy: 0.8790 - val_loss: 1.3107 - val_accuracy: 0.7349 Epoch 508/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1312 - accuracy: 0.9668 - val_loss: 1.5285 - val_accuracy: 0.7456 Epoch 509/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0558 - accuracy: 0.9858 - val_loss: 1.8784 - val_accuracy: 0.7349 Epoch 510/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0650 - accuracy: 0.9834 - val_loss: 1.9990 - val_accuracy: 0.7295 Epoch 511/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0816 - accuracy: 0.9798 - val_loss: 1.9762 - val_accuracy: 0.7278 Epoch 512/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0321 - accuracy: 0.9929 - val_loss: 2.5815 - val_accuracy: 0.7100 Epoch 513/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0329 - accuracy: 0.9941 - val_loss: 2.3716 - val_accuracy: 0.7331 Epoch 514/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0417 - accuracy: 0.9893 - val_loss: 2.6678 - val_accuracy: 0.7242 Epoch 515/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0213 - accuracy: 0.9941 - val_loss: 2.5519 - val_accuracy: 0.7278 Epoch 516/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0935 - accuracy: 0.9763 - val_loss: 2.0074 - val_accuracy: 0.7420 Epoch 517/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0763 - accuracy: 0.9822 - val_loss: 2.0987 - val_accuracy: 0.7367 Epoch 518/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0334 - accuracy: 0.9953 - val_loss: 2.3988 - val_accuracy: 0.7153 Epoch 519/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0454 - accuracy: 0.9893 - val_loss: 2.2744 - val_accuracy: 0.7153 Epoch 520/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0563 - accuracy: 0.9822 - val_loss: 1.6783 - val_accuracy: 0.7687 Epoch 521/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0733 - accuracy: 0.9798 - val_loss: 2.0862 - val_accuracy: 0.7491 Epoch 522/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0367 - accuracy: 0.9893 - val_loss: 2.2574 - val_accuracy: 0.7633 Epoch 523/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0155 - accuracy: 0.9953 - val_loss: 1.9720 - val_accuracy: 0.7651 Epoch 524/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0074 - accuracy: 0.9988 - val_loss: 2.1408 - val_accuracy: 0.7651 Epoch 525/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9988 - val_loss: 2.2524 - val_accuracy: 0.7562 Epoch 526/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 2.3184 - val_accuracy: 0.7527 Epoch 527/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 2.4034 - val_accuracy: 0.7491 Epoch 528/750 27/27 [==============================] - 0s 2ms/step - loss: 8.3055e-04 - accuracy: 1.0000 - val_loss: 2.4531 - val_accuracy: 0.7544 Epoch 529/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0219 - accuracy: 0.9976 - val_loss: 2.3304 - val_accuracy: 0.7527 Epoch 530/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 2.3426 - val_accuracy: 0.7598 Epoch 531/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 2.4192 - val_accuracy: 0.7616 Epoch 532/750 27/27 [==============================] - 0s 2ms/step - loss: 8.9821e-04 - accuracy: 1.0000 - val_loss: 2.4848 - val_accuracy: 0.7580 Epoch 533/750 27/27 [==============================] - 0s 2ms/step - loss: 4.4918e-04 - accuracy: 1.0000 - val_loss: 2.5352 - val_accuracy: 0.7562 Epoch 534/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0029 - accuracy: 0.9988 - val_loss: 2.5932 - val_accuracy: 0.7527 Epoch 535/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 2.7049 - val_accuracy: 0.7562 Epoch 536/750 27/27 [==============================] - 0s 2ms/step - loss: 3.4181e-04 - accuracy: 1.0000 - val_loss: 2.7317 - val_accuracy: 0.7562 Epoch 537/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0161 - accuracy: 0.9964 - val_loss: 2.5887 - val_accuracy: 0.7687 Epoch 538/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0098 - accuracy: 0.9988 - val_loss: 2.7110 - val_accuracy: 0.7473 Epoch 539/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0244 - accuracy: 0.9929 - val_loss: 2.6536 - val_accuracy: 0.7544 Epoch 540/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 2.6927 - val_accuracy: 0.7509 Epoch 541/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0128 - accuracy: 0.9988 - val_loss: 2.6865 - val_accuracy: 0.7544 Epoch 542/750 27/27 [==============================] - 0s 2ms/step - loss: 7.2742e-04 - accuracy: 1.0000 - val_loss: 2.7028 - val_accuracy: 0.7509 Epoch 543/750 27/27 [==============================] - 0s 2ms/step - loss: 7.9764e-04 - accuracy: 1.0000 - val_loss: 2.7536 - val_accuracy: 0.7544 Epoch 544/750 27/27 [==============================] - 0s 3ms/step - loss: 4.8460e-04 - accuracy: 1.0000 - val_loss: 2.8016 - val_accuracy: 0.7527 Epoch 545/750 27/27 [==============================] - 0s 2ms/step - loss: 3.3598e-04 - accuracy: 1.0000 - val_loss: 2.8590 - val_accuracy: 0.7509 Epoch 546/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0203 - accuracy: 0.9988 - val_loss: 3.3397 - val_accuracy: 0.7367 Epoch 547/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0357 - accuracy: 0.9964 - val_loss: 2.7623 - val_accuracy: 0.7598 Epoch 548/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0108 - accuracy: 0.9964 - val_loss: 2.6253 - val_accuracy: 0.7562 Epoch 549/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0234 - accuracy: 0.9964 - val_loss: 2.7098 - val_accuracy: 0.7260 Epoch 550/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0382 - accuracy: 0.9941 - val_loss: 3.0071 - val_accuracy: 0.7224 Epoch 551/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2084 - accuracy: 0.9549 - val_loss: 2.2595 - val_accuracy: 0.7082 Epoch 552/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1698 - accuracy: 0.9656 - val_loss: 1.8274 - val_accuracy: 0.7260 Epoch 553/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0708 - accuracy: 0.9822 - val_loss: 1.9036 - val_accuracy: 0.7438 Epoch 554/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0702 - accuracy: 0.9858 - val_loss: 1.9988 - val_accuracy: 0.7046 Epoch 555/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0854 - accuracy: 0.9751 - val_loss: 2.0525 - val_accuracy: 0.7295 Epoch 556/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0349 - accuracy: 0.9917 - val_loss: 2.1537 - val_accuracy: 0.7384 Epoch 557/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0117 - accuracy: 0.9976 - val_loss: 2.3012 - val_accuracy: 0.7438 Epoch 558/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 2.2300 - val_accuracy: 0.7420 Epoch 559/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0049 - accuracy: 0.9988 - val_loss: 2.3107 - val_accuracy: 0.7527 Epoch 560/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 0.9988 - val_loss: 2.5353 - val_accuracy: 0.7456 Epoch 561/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 2.4969 - val_accuracy: 0.7438 Epoch 562/750 27/27 [==============================] - 0s 2ms/step - loss: 9.9969e-04 - accuracy: 1.0000 - val_loss: 2.5027 - val_accuracy: 0.7473 Epoch 563/750 27/27 [==============================] - 0s 2ms/step - loss: 7.9256e-04 - accuracy: 1.0000 - val_loss: 2.5415 - val_accuracy: 0.7473 Epoch 564/750 27/27 [==============================] - 0s 2ms/step - loss: 9.8366e-04 - accuracy: 1.0000 - val_loss: 2.5607 - val_accuracy: 0.7491 Epoch 565/750 27/27 [==============================] - 0s 2ms/step - loss: 9.7878e-04 - accuracy: 1.0000 - val_loss: 2.5680 - val_accuracy: 0.7473 Epoch 566/750 27/27 [==============================] - 0s 3ms/step - loss: 3.7221e-04 - accuracy: 1.0000 - val_loss: 2.6336 - val_accuracy: 0.7509 Epoch 567/750 27/27 [==============================] - 0s 2ms/step - loss: 4.5236e-04 - accuracy: 1.0000 - val_loss: 2.6543 - val_accuracy: 0.7509 Epoch 568/750 27/27 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.6964 - val_accuracy: 0.7509 Epoch 569/750 27/27 [==============================] - 0s 2ms/step - loss: 4.1607e-04 - accuracy: 1.0000 - val_loss: 2.7692 - val_accuracy: 0.7527 Epoch 570/750 27/27 [==============================] - 0s 2ms/step - loss: 3.8522e-04 - accuracy: 1.0000 - val_loss: 2.7806 - val_accuracy: 0.7544 Epoch 571/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0071 - accuracy: 0.9988 - val_loss: 2.7306 - val_accuracy: 0.7544 Epoch 572/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0092 - accuracy: 0.9976 - val_loss: 2.6173 - val_accuracy: 0.7420 Epoch 573/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0014 - accuracy: 0.9988 - val_loss: 2.6973 - val_accuracy: 0.7491 Epoch 574/750 27/27 [==============================] - 0s 2ms/step - loss: 4.6206e-04 - accuracy: 1.0000 - val_loss: 2.7839 - val_accuracy: 0.7527 Epoch 575/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 2.8164 - val_accuracy: 0.7527 Epoch 576/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 2.8249 - val_accuracy: 0.7580 Epoch 577/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.8376 - val_accuracy: 0.7616 Epoch 578/750 27/27 [==============================] - 0s 2ms/step - loss: 8.6646e-04 - accuracy: 1.0000 - val_loss: 2.8366 - val_accuracy: 0.7616 Epoch 579/750 27/27 [==============================] - 0s 2ms/step - loss: 5.4573e-04 - accuracy: 1.0000 - val_loss: 2.8571 - val_accuracy: 0.7616 Epoch 580/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0015 - accuracy: 0.9988 - val_loss: 2.9241 - val_accuracy: 0.7544 Epoch 581/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 2.9572 - val_accuracy: 0.7580 Epoch 582/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.9436 - val_accuracy: 0.7580 Epoch 583/750 27/27 [==============================] - 0s 2ms/step - loss: 4.6557e-04 - accuracy: 1.0000 - val_loss: 2.9781 - val_accuracy: 0.7598 Epoch 584/750 27/27 [==============================] - 0s 2ms/step - loss: 5.0972e-04 - accuracy: 1.0000 - val_loss: 2.9868 - val_accuracy: 0.7580 Epoch 585/750 27/27 [==============================] - 0s 2ms/step - loss: 4.7463e-04 - accuracy: 1.0000 - val_loss: 3.0257 - val_accuracy: 0.7580 Epoch 586/750 27/27 [==============================] - 0s 2ms/step - loss: 1.9919e-04 - accuracy: 1.0000 - val_loss: 3.0488 - val_accuracy: 0.7580 Epoch 587/750 27/27 [==============================] - 0s 2ms/step - loss: 9.4528e-04 - accuracy: 1.0000 - val_loss: 3.0147 - val_accuracy: 0.7580 Epoch 588/750 27/27 [==============================] - 0s 2ms/step - loss: 2.1540e-04 - accuracy: 1.0000 - val_loss: 3.0328 - val_accuracy: 0.7544 Epoch 589/750 27/27 [==============================] - 0s 2ms/step - loss: 2.1663e-04 - accuracy: 1.0000 - val_loss: 3.0401 - val_accuracy: 0.7562 Epoch 590/750 27/27 [==============================] - 0s 2ms/step - loss: 2.2311e-04 - accuracy: 1.0000 - val_loss: 3.0566 - val_accuracy: 0.7580 Epoch 591/750 27/27 [==============================] - 0s 2ms/step - loss: 7.1746e-04 - accuracy: 1.0000 - val_loss: 3.0566 - val_accuracy: 0.7580 Epoch 592/750 27/27 [==============================] - 0s 2ms/step - loss: 2.9370e-04 - accuracy: 1.0000 - val_loss: 3.0732 - val_accuracy: 0.7544 Epoch 593/750 27/27 [==============================] - 0s 2ms/step - loss: 1.2228e-04 - accuracy: 1.0000 - val_loss: 3.1039 - val_accuracy: 0.7509 Epoch 594/750 27/27 [==============================] - 0s 2ms/step - loss: 2.0016e-04 - accuracy: 1.0000 - val_loss: 3.1172 - val_accuracy: 0.7509 Epoch 595/750 27/27 [==============================] - 0s 2ms/step - loss: 2.0001e-04 - accuracy: 1.0000 - val_loss: 3.1427 - val_accuracy: 0.7491 Epoch 596/750 27/27 [==============================] - 0s 2ms/step - loss: 8.1961e-05 - accuracy: 1.0000 - val_loss: 3.1622 - val_accuracy: 0.7473 Epoch 597/750 27/27 [==============================] - 0s 2ms/step - loss: 5.7705e-04 - accuracy: 1.0000 - val_loss: 3.1994 - val_accuracy: 0.7491 Epoch 598/750 27/27 [==============================] - 0s 2ms/step - loss: 1.0096e-04 - accuracy: 1.0000 - val_loss: 3.2237 - val_accuracy: 0.7509 Epoch 599/750 27/27 [==============================] - 0s 2ms/step - loss: 1.8436e-04 - accuracy: 1.0000 - val_loss: 3.2563 - val_accuracy: 0.7491 Epoch 600/750 27/27 [==============================] - 0s 2ms/step - loss: 7.7130e-05 - accuracy: 1.0000 - val_loss: 3.2723 - val_accuracy: 0.7491 Epoch 601/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0028 - accuracy: 0.9988 - val_loss: 3.2585 - val_accuracy: 0.7473 Epoch 602/750 27/27 [==============================] - 0s 2ms/step - loss: 2.0931e-04 - accuracy: 1.0000 - val_loss: 3.3025 - val_accuracy: 0.7509 Epoch 603/750 27/27 [==============================] - 0s 2ms/step - loss: 7.3902e-05 - accuracy: 1.0000 - val_loss: 3.3309 - val_accuracy: 0.7527 Epoch 604/750 27/27 [==============================] - 0s 2ms/step - loss: 1.3499e-04 - accuracy: 1.0000 - val_loss: 3.3663 - val_accuracy: 0.7544 Epoch 605/750 27/27 [==============================] - 0s 2ms/step - loss: 2.8479e-04 - accuracy: 1.0000 - val_loss: 3.3941 - val_accuracy: 0.7562 Epoch 606/750 27/27 [==============================] - 0s 2ms/step - loss: 4.4744e-04 - accuracy: 1.0000 - val_loss: 3.4160 - val_accuracy: 0.7544 Epoch 607/750 27/27 [==============================] - 0s 2ms/step - loss: 1.7857e-04 - accuracy: 1.0000 - val_loss: 3.4026 - val_accuracy: 0.7527 Epoch 608/750 27/27 [==============================] - 0s 2ms/step - loss: 1.1653e-04 - accuracy: 1.0000 - val_loss: 3.6712 - val_accuracy: 0.7438 Epoch 609/750 27/27 [==============================] - 0s 2ms/step - loss: 8.9028e-04 - accuracy: 1.0000 - val_loss: 3.7081 - val_accuracy: 0.7402 Epoch 610/750 27/27 [==============================] - 0s 2ms/step - loss: 1.6847e-04 - accuracy: 1.0000 - val_loss: 3.7128 - val_accuracy: 0.7420 Epoch 611/750 27/27 [==============================] - 0s 2ms/step - loss: 7.6159e-05 - accuracy: 1.0000 - val_loss: 3.8000 - val_accuracy: 0.7598 Epoch 612/750 27/27 [==============================] - 0s 2ms/step - loss: 1.2537e-04 - accuracy: 1.0000 - val_loss: 3.8048 - val_accuracy: 0.7633 Epoch 613/750 27/27 [==============================] - 0s 2ms/step - loss: 1.5084e-04 - accuracy: 1.0000 - val_loss: 3.7526 - val_accuracy: 0.7616 Epoch 614/750 27/27 [==============================] - 0s 2ms/step - loss: 8.4907e-05 - accuracy: 1.0000 - val_loss: 3.7652 - val_accuracy: 0.7633 Epoch 615/750 27/27 [==============================] - 0s 2ms/step - loss: 7.7931e-05 - accuracy: 1.0000 - val_loss: 3.7731 - val_accuracy: 0.7616 Epoch 616/750 27/27 [==============================] - 0s 2ms/step - loss: 2.0494e-04 - accuracy: 1.0000 - val_loss: 3.7557 - val_accuracy: 0.7633 Epoch 617/750 27/27 [==============================] - 0s 2ms/step - loss: 4.2542e-05 - accuracy: 1.0000 - val_loss: 3.7642 - val_accuracy: 0.7651 Epoch 618/750 27/27 [==============================] - 0s 2ms/step - loss: 4.1767e-05 - accuracy: 1.0000 - val_loss: 3.7756 - val_accuracy: 0.7616 Epoch 619/750 27/27 [==============================] - 0s 2ms/step - loss: 2.5256e-04 - accuracy: 1.0000 - val_loss: 3.8063 - val_accuracy: 0.7633 Epoch 620/750 27/27 [==============================] - 0s 2ms/step - loss: 4.3807e-04 - accuracy: 1.0000 - val_loss: 3.8946 - val_accuracy: 0.7527 Epoch 621/750 27/27 [==============================] - 0s 2ms/step - loss: 1.3915e-04 - accuracy: 1.0000 - val_loss: 4.0011 - val_accuracy: 0.7438 Epoch 622/750 27/27 [==============================] - 0s 2ms/step - loss: 1.0801e-04 - accuracy: 1.0000 - val_loss: 4.0031 - val_accuracy: 0.7438 Epoch 623/750 27/27 [==============================] - 0s 2ms/step - loss: 4.4528e-05 - accuracy: 1.0000 - val_loss: 4.0209 - val_accuracy: 0.7473 Epoch 624/750 27/27 [==============================] - 0s 2ms/step - loss: 9.1955e-05 - accuracy: 1.0000 - val_loss: 3.9380 - val_accuracy: 0.7509 Epoch 625/750 27/27 [==============================] - 0s 2ms/step - loss: 4.4252e-04 - accuracy: 1.0000 - val_loss: 3.8808 - val_accuracy: 0.7544 Epoch 626/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0241 - accuracy: 0.9964 - val_loss: 3.8762 - val_accuracy: 0.7491 Epoch 627/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1064 - accuracy: 0.9775 - val_loss: 2.8774 - val_accuracy: 0.7135 Epoch 628/750 27/27 [==============================] - 0s 2ms/step - loss: 0.4220 - accuracy: 0.8980 - val_loss: 1.6709 - val_accuracy: 0.7206 Epoch 629/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2078 - accuracy: 0.9395 - val_loss: 1.9308 - val_accuracy: 0.7153 Epoch 630/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0384 - accuracy: 0.9905 - val_loss: 2.1386 - val_accuracy: 0.7278 Epoch 631/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1174 - accuracy: 0.9775 - val_loss: 1.9405 - val_accuracy: 0.7260 Epoch 632/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0852 - accuracy: 0.9810 - val_loss: 2.0875 - val_accuracy: 0.7082 Epoch 633/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0194 - accuracy: 0.9964 - val_loss: 1.8331 - val_accuracy: 0.7598 Epoch 634/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 2.0458 - val_accuracy: 0.7562 Epoch 635/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 2.1281 - val_accuracy: 0.7598 Epoch 636/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 2.1867 - val_accuracy: 0.7598 Epoch 637/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 2.2659 - val_accuracy: 0.7580 Epoch 638/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9988 - val_loss: 2.3057 - val_accuracy: 0.7598 Epoch 639/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9988 - val_loss: 2.3325 - val_accuracy: 0.7616 Epoch 640/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 2.3844 - val_accuracy: 0.7633 Epoch 641/750 27/27 [==============================] - 0s 2ms/step - loss: 9.5624e-04 - accuracy: 1.0000 - val_loss: 2.4332 - val_accuracy: 0.7616 Epoch 642/750 27/27 [==============================] - 0s 2ms/step - loss: 4.4369e-04 - accuracy: 1.0000 - val_loss: 2.4714 - val_accuracy: 0.7616 Epoch 643/750 27/27 [==============================] - 0s 2ms/step - loss: 4.0203e-04 - accuracy: 1.0000 - val_loss: 2.5042 - val_accuracy: 0.7598 Epoch 644/750 27/27 [==============================] - 0s 2ms/step - loss: 9.4873e-04 - accuracy: 1.0000 - val_loss: 2.5398 - val_accuracy: 0.7580 Epoch 645/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 2.6343 - val_accuracy: 0.7562 Epoch 646/750 27/27 [==============================] - 0s 2ms/step - loss: 4.0136e-04 - accuracy: 1.0000 - val_loss: 2.6885 - val_accuracy: 0.7616 Epoch 647/750 27/27 [==============================] - 0s 2ms/step - loss: 4.4283e-04 - accuracy: 1.0000 - val_loss: 2.7244 - val_accuracy: 0.7598 Epoch 648/750 27/27 [==============================] - 0s 2ms/step - loss: 4.7468e-04 - accuracy: 1.0000 - val_loss: 2.7637 - val_accuracy: 0.7616 Epoch 649/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0054 - accuracy: 0.9976 - val_loss: 2.6782 - val_accuracy: 0.7580 Epoch 650/750 27/27 [==============================] - 0s 2ms/step - loss: 4.3588e-04 - accuracy: 1.0000 - val_loss: 2.6535 - val_accuracy: 0.7527 Epoch 651/750 27/27 [==============================] - 0s 2ms/step - loss: 8.4109e-04 - accuracy: 1.0000 - val_loss: 2.6847 - val_accuracy: 0.7580 Epoch 652/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0127 - accuracy: 0.9988 - val_loss: 2.6245 - val_accuracy: 0.7544 Epoch 653/750 27/27 [==============================] - 0s 2ms/step - loss: 5.8848e-04 - accuracy: 1.0000 - val_loss: 2.6546 - val_accuracy: 0.7473 Epoch 654/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 0.9988 - val_loss: 2.6741 - val_accuracy: 0.7562 Epoch 655/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0049 - accuracy: 0.9988 - val_loss: 2.5156 - val_accuracy: 0.7580 Epoch 656/750 27/27 [==============================] - 0s 2ms/step - loss: 3.2030e-04 - accuracy: 1.0000 - val_loss: 2.5429 - val_accuracy: 0.7562 Epoch 657/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.6216 - val_accuracy: 0.7562 Epoch 658/750 27/27 [==============================] - 0s 2ms/step - loss: 6.5362e-04 - accuracy: 1.0000 - val_loss: 2.7036 - val_accuracy: 0.7527 Epoch 659/750 27/27 [==============================] - 0s 2ms/step - loss: 3.6187e-04 - accuracy: 1.0000 - val_loss: 2.7133 - val_accuracy: 0.7562 Epoch 660/750 27/27 [==============================] - 0s 2ms/step - loss: 5.2855e-04 - accuracy: 1.0000 - val_loss: 2.7513 - val_accuracy: 0.7580 Epoch 661/750 27/27 [==============================] - 0s 2ms/step - loss: 1.7589e-04 - accuracy: 1.0000 - val_loss: 2.7764 - val_accuracy: 0.7580 Epoch 662/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 2.8095 - val_accuracy: 0.7544 Epoch 663/750 27/27 [==============================] - 0s 2ms/step - loss: 1.9480e-04 - accuracy: 1.0000 - val_loss: 2.8392 - val_accuracy: 0.7544 Epoch 664/750 27/27 [==============================] - 0s 2ms/step - loss: 3.4439e-04 - accuracy: 1.0000 - val_loss: 2.8800 - val_accuracy: 0.7527 Epoch 665/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0028 - accuracy: 0.9988 - val_loss: 2.9012 - val_accuracy: 0.7527 Epoch 666/750 27/27 [==============================] - 0s 2ms/step - loss: 9.9800e-04 - accuracy: 1.0000 - val_loss: 2.8792 - val_accuracy: 0.7509 Epoch 667/750 27/27 [==============================] - 0s 2ms/step - loss: 1.3046e-04 - accuracy: 1.0000 - val_loss: 2.8968 - val_accuracy: 0.7544 Epoch 668/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9988 - val_loss: 3.0344 - val_accuracy: 0.7473 Epoch 669/750 27/27 [==============================] - 0s 2ms/step - loss: 1.8974e-04 - accuracy: 1.0000 - val_loss: 3.0669 - val_accuracy: 0.7456 Epoch 670/750 27/27 [==============================] - 0s 2ms/step - loss: 3.1324e-04 - accuracy: 1.0000 - val_loss: 3.0891 - val_accuracy: 0.7473 Epoch 671/750 27/27 [==============================] - 0s 2ms/step - loss: 6.2708e-04 - accuracy: 1.0000 - val_loss: 3.1065 - val_accuracy: 0.7473 Epoch 672/750 27/27 [==============================] - 0s 2ms/step - loss: 1.8693e-04 - accuracy: 1.0000 - val_loss: 3.1538 - val_accuracy: 0.7456 Epoch 673/750 27/27 [==============================] - 0s 2ms/step - loss: 1.2799e-04 - accuracy: 1.0000 - val_loss: 3.1543 - val_accuracy: 0.7473 Epoch 674/750 27/27 [==============================] - 0s 2ms/step - loss: 2.3970e-04 - accuracy: 1.0000 - val_loss: 3.1552 - val_accuracy: 0.7438 Epoch 675/750 27/27 [==============================] - 0s 2ms/step - loss: 7.8064e-04 - accuracy: 1.0000 - val_loss: 3.1736 - val_accuracy: 0.7527 Epoch 676/750 27/27 [==============================] - 0s 2ms/step - loss: 7.0209e-04 - accuracy: 1.0000 - val_loss: 3.1884 - val_accuracy: 0.7527 Epoch 677/750 27/27 [==============================] - 0s 2ms/step - loss: 1.0179e-04 - accuracy: 1.0000 - val_loss: 3.1968 - val_accuracy: 0.7544 Epoch 678/750 27/27 [==============================] - 0s 2ms/step - loss: 9.6339e-05 - accuracy: 1.0000 - val_loss: 3.2006 - val_accuracy: 0.7544 Epoch 679/750 27/27 [==============================] - 0s 2ms/step - loss: 9.6900e-05 - accuracy: 1.0000 - val_loss: 3.2094 - val_accuracy: 0.7544 Epoch 680/750 27/27 [==============================] - 0s 2ms/step - loss: 1.0489e-04 - accuracy: 1.0000 - val_loss: 3.2160 - val_accuracy: 0.7544 Epoch 681/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0049 - accuracy: 0.9976 - val_loss: 3.1166 - val_accuracy: 0.7616 Epoch 682/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 2.8825 - val_accuracy: 0.7740 Epoch 683/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1188 - accuracy: 0.9822 - val_loss: 2.5272 - val_accuracy: 0.7331 Epoch 684/750 27/27 [==============================] - 0s 2ms/step - loss: 0.4386 - accuracy: 0.9063 - val_loss: 1.4794 - val_accuracy: 0.6779 Epoch 685/750 27/27 [==============================] - 0s 2ms/step - loss: 0.2475 - accuracy: 0.9265 - val_loss: 1.9435 - val_accuracy: 0.6975 Epoch 686/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0789 - accuracy: 0.9822 - val_loss: 1.9065 - val_accuracy: 0.7313 Epoch 687/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0175 - accuracy: 0.9976 - val_loss: 2.2665 - val_accuracy: 0.7295 Epoch 688/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0157 - accuracy: 0.9964 - val_loss: 2.3646 - val_accuracy: 0.7295 Epoch 689/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0125 - accuracy: 0.9988 - val_loss: 2.6752 - val_accuracy: 0.7189 Epoch 690/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0092 - accuracy: 0.9976 - val_loss: 2.5681 - val_accuracy: 0.7402 Epoch 691/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0178 - accuracy: 0.9976 - val_loss: 2.6059 - val_accuracy: 0.7295 Epoch 692/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0989 - accuracy: 0.9798 - val_loss: 2.1838 - val_accuracy: 0.7331 Epoch 693/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0583 - accuracy: 0.9917 - val_loss: 2.2911 - val_accuracy: 0.7171 Epoch 694/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0455 - accuracy: 0.9905 - val_loss: 2.3563 - val_accuracy: 0.7189 Epoch 695/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1137 - accuracy: 0.9739 - val_loss: 2.5632 - val_accuracy: 0.7011 Epoch 696/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0389 - accuracy: 0.9893 - val_loss: 1.9807 - val_accuracy: 0.7367 Epoch 697/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0398 - accuracy: 0.9929 - val_loss: 2.3263 - val_accuracy: 0.7295 Epoch 698/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0160 - accuracy: 0.9953 - val_loss: 2.2073 - val_accuracy: 0.7456 Epoch 699/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0215 - accuracy: 0.9953 - val_loss: 2.5016 - val_accuracy: 0.7331 Epoch 700/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0216 - accuracy: 0.9953 - val_loss: 2.6409 - val_accuracy: 0.7117 Epoch 701/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0221 - accuracy: 0.9941 - val_loss: 2.6838 - val_accuracy: 0.7278 Epoch 702/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0245 - accuracy: 0.9964 - val_loss: 2.5718 - val_accuracy: 0.7295 Epoch 703/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0277 - accuracy: 0.9929 - val_loss: 2.6175 - val_accuracy: 0.7331 Epoch 704/750 27/27 [==============================] - 0s 2ms/step - loss: 0.1085 - accuracy: 0.9786 - val_loss: 2.2943 - val_accuracy: 0.7509 Epoch 705/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0938 - accuracy: 0.9786 - val_loss: 2.6003 - val_accuracy: 0.7295 Epoch 706/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0192 - accuracy: 0.9964 - val_loss: 2.4311 - val_accuracy: 0.7456 Epoch 707/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0142 - accuracy: 0.9964 - val_loss: 2.6240 - val_accuracy: 0.7242 Epoch 708/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0887 - accuracy: 0.9846 - val_loss: 2.4275 - val_accuracy: 0.7028 Epoch 709/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0675 - accuracy: 0.9870 - val_loss: 2.4290 - val_accuracy: 0.7224 Epoch 710/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0510 - accuracy: 0.9929 - val_loss: 2.2570 - val_accuracy: 0.7313 Epoch 711/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 2.3275 - val_accuracy: 0.7456 Epoch 712/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 2.3492 - val_accuracy: 0.7509 Epoch 713/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 2.4092 - val_accuracy: 0.7473 Epoch 714/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 2.4686 - val_accuracy: 0.7473 Epoch 715/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 2.5274 - val_accuracy: 0.7438 Epoch 716/750 27/27 [==============================] - 0s 2ms/step - loss: 6.8498e-04 - accuracy: 1.0000 - val_loss: 2.6096 - val_accuracy: 0.7402 Epoch 717/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 2.6696 - val_accuracy: 0.7402 Epoch 718/750 27/27 [==============================] - 0s 2ms/step - loss: 7.5848e-04 - accuracy: 1.0000 - val_loss: 2.6625 - val_accuracy: 0.7420 Epoch 719/750 27/27 [==============================] - 0s 2ms/step - loss: 4.2598e-04 - accuracy: 1.0000 - val_loss: 2.6724 - val_accuracy: 0.7420 Epoch 720/750 27/27 [==============================] - 0s 2ms/step - loss: 4.0998e-04 - accuracy: 1.0000 - val_loss: 2.6968 - val_accuracy: 0.7420 Epoch 721/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 2.7634 - val_accuracy: 0.7438 Epoch 722/750 27/27 [==============================] - 0s 2ms/step - loss: 7.2751e-04 - accuracy: 1.0000 - val_loss: 2.8616 - val_accuracy: 0.7420 Epoch 723/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9976 - val_loss: 2.9547 - val_accuracy: 0.7349 Epoch 724/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 3.0088 - val_accuracy: 0.7331 Epoch 725/750 27/27 [==============================] - 0s 2ms/step - loss: 3.3433e-04 - accuracy: 1.0000 - val_loss: 2.9984 - val_accuracy: 0.7331 Epoch 726/750 27/27 [==============================] - 0s 2ms/step - loss: 4.6574e-04 - accuracy: 1.0000 - val_loss: 2.9983 - val_accuracy: 0.7349 Epoch 727/750 27/27 [==============================] - 0s 2ms/step - loss: 2.0618e-04 - accuracy: 1.0000 - val_loss: 3.0129 - val_accuracy: 0.7349 Epoch 728/750 27/27 [==============================] - 0s 2ms/step - loss: 2.2255e-04 - accuracy: 1.0000 - val_loss: 3.0287 - val_accuracy: 0.7367 Epoch 729/750 27/27 [==============================] - 0s 2ms/step - loss: 2.8452e-04 - accuracy: 1.0000 - val_loss: 3.0532 - val_accuracy: 0.7384 Epoch 730/750 27/27 [==============================] - 0s 2ms/step - loss: 1.5129e-04 - accuracy: 1.0000 - val_loss: 3.0747 - val_accuracy: 0.7367 Epoch 731/750 27/27 [==============================] - 0s 2ms/step - loss: 2.1172e-04 - accuracy: 1.0000 - val_loss: 3.0876 - val_accuracy: 0.7367 Epoch 732/750 27/27 [==============================] - 0s 2ms/step - loss: 3.0242e-04 - accuracy: 1.0000 - val_loss: 3.1009 - val_accuracy: 0.7420 Epoch 733/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 0.9988 - val_loss: 3.1036 - val_accuracy: 0.7367 Epoch 734/750 27/27 [==============================] - 0s 2ms/step - loss: 2.9435e-04 - accuracy: 1.0000 - val_loss: 3.1035 - val_accuracy: 0.7402 Epoch 735/750 27/27 [==============================] - 0s 2ms/step - loss: 3.3230e-04 - accuracy: 1.0000 - val_loss: 3.1165 - val_accuracy: 0.7402 Epoch 736/750 27/27 [==============================] - 0s 2ms/step - loss: 5.3612e-04 - accuracy: 1.0000 - val_loss: 3.1414 - val_accuracy: 0.7402 Epoch 737/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0057 - accuracy: 0.9976 - val_loss: 3.2017 - val_accuracy: 0.7402 Epoch 738/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 0.9988 - val_loss: 3.1047 - val_accuracy: 0.7384 Epoch 739/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0252 - accuracy: 0.9953 - val_loss: 3.3888 - val_accuracy: 0.7242 Epoch 740/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0546 - accuracy: 0.9881 - val_loss: 2.7365 - val_accuracy: 0.7295 Epoch 741/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0211 - accuracy: 0.9929 - val_loss: 2.5424 - val_accuracy: 0.7367 Epoch 742/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 2.8026 - val_accuracy: 0.7456 Epoch 743/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 2.8939 - val_accuracy: 0.7473 Epoch 744/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 0.9988 - val_loss: 2.8224 - val_accuracy: 0.7473 Epoch 745/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 2.8255 - val_accuracy: 0.7598 Epoch 746/750 27/27 [==============================] - 0s 2ms/step - loss: 9.5804e-04 - accuracy: 1.0000 - val_loss: 2.8954 - val_accuracy: 0.7544 Epoch 747/750 27/27 [==============================] - 0s 2ms/step - loss: 3.0087e-04 - accuracy: 1.0000 - val_loss: 2.9369 - val_accuracy: 0.7544 Epoch 748/750 27/27 [==============================] - 0s 2ms/step - loss: 3.1512e-04 - accuracy: 1.0000 - val_loss: 2.9854 - val_accuracy: 0.7527 Epoch 749/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 0.9988 - val_loss: 2.9870 - val_accuracy: 0.7527 Epoch 750/750 27/27 [==============================] - 0s 2ms/step - loss: 0.0014 - accuracy: 0.9988 - val_loss: 2.9921 - val_accuracy: 0.7491
model.save("songs_model.keras")
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc)
plt.plot(epochs, val_acc)
plt.legend(["Accuracy", "Val_Accuracy"])
plt.show()
Y_pred = model.predict(X_test)
y_pred = np.argmax(Y_pred, axis = 1)
18/18 [==============================] - 0s 784us/step
r2_score(Y_test, y_pred)
0.4615727999243392
correct = 0
total = 0
for i in range(0, len(y_pred)):
if Y_test[i] == y_pred[i]:
correct += 1
total += 1
round(correct/total, 4) * 100
74.91
cm = confusion_matrix(Y_test, y_pred)
for i in range(0, len(cm[0])):
cm[i] = cm[i] * 100 / float(sum(cm[i]))
cm
array([[76, 7, 0, 2, 0, 2, 7, 0, 2],
[ 1, 81, 10, 1, 0, 0, 1, 3, 1],
[ 1, 14, 73, 3, 0, 1, 2, 0, 4],
[ 4, 0, 12, 46, 0, 21, 0, 7, 7],
[ 2, 2, 11, 0, 76, 0, 0, 5, 0],
[ 0, 0, 8, 8, 0, 56, 4, 17, 4],
[ 0, 1, 2, 0, 0, 0, 93, 0, 0],
[ 1, 2, 8, 4, 1, 1, 0, 70, 9],
[ 4, 16, 4, 2, 0, 6, 14, 0, 54]])
ar = []
for i in range(0, len(np.unique(Y_t))):
ar.append(i)
dl = l.inverse_transform(ar)
dl
array(['ABBA', 'Beach Boys', 'Beatles', 'Bob Dylan', 'Elvis Presley',
'Led Zeppelin', 'Nirvana', 'Pink Floyd', 'Queen'], dtype='<U13')
cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = np.char.lower(dl))
cm_display.plot()
plt.title("Confusion Matrix")
plt.xticks(rotation=90)
plt.show()
def prediction(filename):
mfcc = features_extractor(filename)
time = duration(filename)
mfcc = np.append(mfcc, time)
pred = model.predict(np.array([mfcc]))[0]
arr = np.argsort(-pred)
arr = arr[:5]
index = arr[0]
y_t = pred[arr] * 100
arr = l.inverse_transform(arr)
na = l.inverse_transform([index])[0]
file = "Pics/" + na + ".jpg"
# songs = os.listdir("Training_Files/" + na)
# r = random.randint(0, len(songs) - 1)
# fname = "Training_Files/" + na + "/" + songs[r]
# soundna = ipd.Audio(fname)
# soundfn = ipd.Audio(filename)
plt.imshow(cv2.imread(file))
plt.show()
# print(na + " Example")
# display(soundna)
# print("Original Song")
# display(soundfn)
D = dict(zip(np.char.upper(arr), y_t))
return D
prediction("Training_Files/Beatles/SheLovesYouRemastered2009.wav")
1/1 [==============================] - 0s 14ms/step
{'BEATLES': 100.0,
'BEACH BOYS': 6.4731546e-07,
'ELVIS PRESLEY': 1.0829525e-09,
'PINK FLOYD': 7.123964e-10,
'NIRVANA': 4.5190177e-10}
prediction("Training_Files/Nirvana/DrainYou.wav")
1/1 [==============================] - 0s 13ms/step
{'NIRVANA': 100.0,
'LED ZEPPELIN': 1.8606692e-15,
'QUEEN': 9.907608e-16,
'ABBA': 3.4589536e-22,
'BEATLES': 6.5398825e-26}
prediction("Linda/YoureNoGood.wav")
1/1 [==============================] - 0s 12ms/step
{'BEACH BOYS': 98.36874,
'PINK FLOYD': 0.90323025,
'QUEEN': 0.5444898,
'BEATLES': 0.17494287,
'NIRVANA': 0.008595253}
prediction("Linda/BlueBayou.wav")
1/1 [==============================] - 0s 13ms/step
{'PINK FLOYD': 97.80583,
'BOB DYLAN': 1.5199271,
'BEATLES': 0.40621528,
'LED ZEPPELIN': 0.112754375,
'BEACH BOYS': 0.089594826}
prediction("Linda/ThatllBetheDay.wav")
1/1 [==============================] - 0s 13ms/step
{'PINK FLOYD': 50.24096,
'QUEEN': 36.67983,
'BEATLES': 6.1744504,
'BOB DYLAN': 1.8168545,
'ABBA': 1.7107702}
prediction("Olivia/vampire.wav")
1/1 [==============================] - 0s 14ms/step
{'LED ZEPPELIN': 97.86497,
'QUEEN': 0.89748913,
'NIRVANA': 0.7243379,
'BOB DYLAN': 0.24101114,
'PINK FLOYD': 0.18197283}
prediction("Olivia/happier.wav")
1/1 [==============================] - 0s 13ms/step
{'BEACH BOYS': 75.98029,
'BEATLES': 22.77097,
'PINK FLOYD': 0.95106333,
'NIRVANA': 0.1913342,
'QUEEN': 0.10547596}
prediction("Olivia/badidearight.wav")
1/1 [==============================] - 0s 13ms/step
{'NIRVANA': 97.52778,
'LED ZEPPELIN': 1.3726364,
'QUEEN': 0.88557667,
'ABBA': 0.11487187,
'BEATLES': 0.06337819}
prediction("Lana/SummertimeSadness.wav")
1/1 [==============================] - 0s 13ms/step
{'PINK FLOYD': 97.71829,
'BEATLES': 0.8376042,
'BEACH BOYS': 0.52598935,
'QUEEN': 0.37064466,
'LED ZEPPELIN': 0.27663594}
prediction("Lana/DietMountainDew.wav")
1/1 [==============================] - 0s 13ms/step
{'ABBA': 91.74855,
'BOB DYLAN': 3.9423914,
'LED ZEPPELIN': 3.0816143,
'QUEEN': 0.84092003,
'PINK FLOYD': 0.21689452}
prediction("Lana/OffToTheRaces.wav")
1/1 [==============================] - 0s 13ms/step
{'ABBA': 99.99597,
'BOB DYLAN': 0.0028721762,
'LED ZEPPELIN': 0.0007155619,
'QUEEN': 0.0004398899,
'PINK FLOYD': 3.2195221e-06}
prediction("Trial_Songs/Nessa Barrett - die first (official lyric video).wav")
1/1 [==============================] - 0s 13ms/step
{'NIRVANA': 99.99523,
'LED ZEPPELIN': 0.004050183,
'QUEEN': 0.0006745665,
'ABBA': 3.9137292e-05,
'BEATLES': 4.566028e-07}
prediction("Trial_Songs/The Neighbourhood - Sweater Weather (Official Video).wav")
1/1 [==============================] - 0s 13ms/step
{'NIRVANA': 99.437546,
'LED ZEPPELIN': 0.4332466,
'QUEEN': 0.10312057,
'ABBA': 0.022964295,
'BOB DYLAN': 0.0014360381}
prediction("Trial_Songs/505.wav")
1/1 [==============================] - 0s 13ms/step
{'PINK FLOYD': 79.67384,
'BEATLES': 7.5573206,
'QUEEN': 3.74495,
'LED ZEPPELIN': 3.1211355,
'BEACH BOYS': 2.8624616}
prediction("Trial_Songs/Dua Lipa - Levitating Featuring DaBaby (Official Music Video).wav")
1/1 [==============================] - 0s 14ms/step
{'ABBA': 99.99993,
'BOB DYLAN': 6.525918e-05,
'LED ZEPPELIN': 6.739596e-06,
'QUEEN': 2.1464512e-06,
'PINK FLOYD': 3.866854e-09}